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| Abstract = {Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically. Unfortunately, models trained purely on rendered images often fail to generalize to real images. To address this shortcoming, prior work introduced unsupervised domain adaptation algorithms that attempt to map representations between the two domains or learn to extract features that are domain-invariant. In this work, we present a new approach that learns, in an unsupervised manner, a transformation in the pixel space from one domain to the other. Our generative adversarial network (GAN)-based method adapts source-domain images to appear as if drawn from the target domain. Our approach not only produces plausible samples, but also outperforms the state-of-the-art on a number of unsupervised domain adaptation scenarios by large margins. Finally, we demonstrate that the adaptation process generalizes to object classes unseen during training.}, | |
| Annote = {use for cogan also}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1612.05424}, | |
| Doi = {10.1109/CVPR.2017.18}, | |
| Eprint = {1612.05424}, | |
| File = {:data/mendeley/Bousmalis{\_}Unsupervised{\_}Pixel-Level{\_}Domain{\_}CVPR{\_}2017{\_}paper.pdf:pdf}, | |
| ISBN = {978-1-5386-0457-1}, | |
| ISSN = {1063-6919}, | |
| Mendeley-groups = {ganthing/translation}, | |
| Url = {http://arxiv.org/abs/1612.05424} | |
| } | |
| @Article{caiCS10SIAMjo, | |
| Title = {A Singular Value Thresholding Algorithm for Matrix Completion}, | |
| Author = {Cai, J. and Cand\'es, E. and Shen, Z.}, | |
| Journal = {SIAM Journal on Optimization}, | |
| Year = {2010}, | |
| Number = {4}, | |
| Pages = {1956-1982}, | |
| Volume = {20}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.06.02} | |
| } | |
| @Article{candes2011rpca, | |
| Title = {Robust Principal Component Analysis?}, | |
| Author = {Cand\'es, E. and Li, X. and Ma, Y. and Wright, J.}, | |
| Journal = {Journal of the ACM}, | |
| Year = {2011}, | |
| Number = {3}, | |
| Volume = {58}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.03.07} | |
| } | |
| @Article{CC01a, | |
| Title = {{LIBSVM}: A library for support vector machines}, | |
| Author = {Chang, Chih-Chung and Lin, Chih-Jen}, | |
| Journal = {ACM Transactions on Intelligent Systems and Technology}, | |
| Year = {2011}, | |
| Pages = {27:1--27:27}, | |
| Volume = {2}, | |
| Issue = {3} | |
| } | |
| @Article{chen2012marginalized, | |
| Title = {Marginalized denoising autoencoders for domain adaptation}, | |
| Author = {Chen, Minmin and Xu, Zhixiang and Weinberger, Kilian and Sha, Fei}, | |
| Journal = {arXiv preprint arXiv:1206.4683}, | |
| Year = {2012} | |
| } | |
| @Article{chen2017learning, | |
| Title = {Learning with Privileged Information for Multi-Label Classification}, | |
| Author = {Chen, Shiyu and Wang, Shangfei and Chen, Tanfang and Shi, Xiaoxiao}, | |
| Journal = {arXiv preprint arXiv:1703.09911}, | |
| Year = {2017} | |
| } | |
| @Article{choi2017stargan, | |
| Title = {StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation}, | |
| Author = {Choi, Yunjey and Choi, Minje and Kim, Munyoung and Ha, Jung-Woo and Kim, Sunghun and Choo, Jaegul}, | |
| Journal = {arXiv preprint arXiv:1711.09020}, | |
| Year = {2017} | |
| } | |
| @Article{crammerS01jmlr, | |
| Title = {On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines}, | |
| Author = {Crammer, K. and Singer, Y.}, | |
| Journal = {JMLR}, | |
| Year = {2001}, | |
| Pages = {265--292}, | |
| Volume = {2} | |
| } | |
| @Article{daume2006domain, | |
| Title = {Domain adaptation for statistical classifiers}, | |
| Author = {Daume III, Hal and Marcu, Daniel}, | |
| Journal = {Journal of Artificial Intelligence Research}, | |
| Year = {2006}, | |
| Pages = {101--126}, | |
| Volume = {26} | |
| } | |
| @Article{deng2014autoencoder, | |
| Title = {Autoencoder-based unsupervised domain adaptation for speech emotion recognition}, | |
| Author = {Deng, Jun and Zhang, Zixing and Eyben, Florian and Schuller, Bjorn}, | |
| Journal = {IEEE Signal Processing Letters}, | |
| Year = {2014}, | |
| Number = {9}, | |
| Pages = {1068--1072}, | |
| Volume = {21}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{deng2017image, | |
| Title = {Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification}, | |
| Author = {Deng, Weijian and Zheng, Liang and Kang, Guoliang and Yang, Yi and Ye, Qixiang and Jiao, Jianbin}, | |
| Journal = {arXiv preprint arXiv:1711.07027}, | |
| Year = {2017} | |
| } | |
| @Article{dengHG12IEEEtpami, | |
| Title = {Extended {SRC}: Undersampled Face Recognition via Intraclass Variant Dictionary}, | |
| Author = {Deng, W. and Hu, J. and Guo, J.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2012}, | |
| Number = {9}, | |
| Pages = {1864-1870}, | |
| Volume = {34}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.06.01} | |
| } | |
| @Article{deshmukhmulticlass, | |
| Title = {Multiclass Domain Generalization}, | |
| Author = {Deshmukh, Aniket Anand and Sharma, Srinagesh and Cutler, James W and Scott, Clayton} | |
| } | |
| @Article{ding2017robust, | |
| Title = {Robust Transfer Metric Learning for Image Classification}, | |
| Author = {Ding, Zhengming and Fu, Yun}, | |
| Journal = {IEEE Transactions on Image Processing}, | |
| Year = {2017}, | |
| Number = {2}, | |
| Pages = {660--670}, | |
| Volume = {26}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{Distance2009Weinberger, | |
| Title = {Distance Metric Learning for Large Margin Nearest Neighbor Classification}, | |
| Author = {Weinberger, K. Q. and Saul, L. K.}, | |
| Journal = {JMLR}, | |
| Year = {2009}, | |
| Month = jun, | |
| Pages = {207--244}, | |
| Volume = {10}, | |
| Acmid = {1577078}, | |
| ISSN = {1532-4435}, | |
| Issue_date = {12/1/2009}, | |
| Numpages = {38}, | |
| Publisher = {JMLR.org}, | |
| Url = {http://dl.acm.org/citation.cfm?id=1577069.1577078} | |
| } | |
| @Article{duanXT12tnnls, | |
| Title = {Domain Adaptation From Multiple Sources: A Domain-Dependent Regularization Approach}, | |
| Author = {Duan, L. and Xu, D. and Tsang, I. W. H.}, | |
| Journal = {IEEE TNNLS}, | |
| Year = {2012}, | |
| Number = {3}, | |
| Pages = {504-518}, | |
| Volume = {23} | |
| } | |
| @Article{Efros2001, | |
| Title = {{Image quilting for texture synthesis and transfer}}, | |
| Author = {Efros, Alexei A. and Freeman, William T.}, | |
| Journal = {Proceedings of the 28th annual conference on Computer graphics and interactive techniques - SIGGRAPH '01}, | |
| Year = {2001}, | |
| Number = {August}, | |
| Pages = {341--346}, | |
| Abstract = {We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces surprisingly good results for a wide range of textures. Second, we extend the algorithm to perform texture transfer — rendering an object with a texture taken from a different object. More generally, we demonstrate how an image can be re-rendered in the style of a different image. The method works directly on the images and does not require 3D information.}, | |
| Doi = {10.1145/383259.383296}, | |
| File = {:data/mendeley/p341-efros.pdf:pdf}, | |
| ISBN = {158113374X}, | |
| ISSN = {00134694}, | |
| Mendeley-groups = {ganthing}, | |
| Url = {http://portal.acm.org/citation.cfm?doid=383259.383296} | |
| } | |
| @Article{Elhamifar2013Sparse, | |
| Title = {Sparse Subspace Clustering: Algorithm, Theory, and Applications}, | |
| Author = {Elhamifar, E. and Vidal, R.}, | |
| Journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, | |
| Year = {2013}, | |
| Month = {Nov}, | |
| Number = {11}, | |
| Pages = {2765-2781}, | |
| Volume = {35}, | |
| Doi = {10.1109/TPAMI.2013.57}, | |
| ISSN = {0162-8828}, | |
| Keywords = {computational complexity;convex programming;data structures;minimisation;pattern clustering;convex relaxation;data point clustering;data point representation;face clustering;general NP-hard problem;high-dimensional data collection;minimization program;motion segmentation;sparse optimization program;sparse representation;sparse subspace clustering algorithm;spectral clustering framework;synthetic data;Clustering algorithms;Computer vision;Face;Noise;Optimization;Sparse matrices;Vectors;$(ell_1)$-minimization;High-dimensional data;clustering;convex programming;face clustering;intrinsic low-dimensionality;motion segmentation;principal angles;sparse representation;spectral clustering;subspaces} | |
| } | |
| @Article{everingham2010pascal, | |
| Title = {The pascal visual object classes (voc) challenge}, | |
| Author = {Everingham, Mark and Van Gool, Luc and Williams, Christopher KI and Winn, John and Zisserman, Andrew}, | |
| Journal = {International journal of computer vision}, | |
| Year = {2010}, | |
| Number = {2}, | |
| Pages = {303--338}, | |
| Volume = {88}, | |
| Publisher = {Springer} | |
| } | |
| @Article{fan08lmlr, | |
| Title = {{LIBLINEAR: A} library for large linear classification}, | |
| Author = {Fan, R.-E. and Chang, K.-W. and Hsieh, C.-J. and Wang, X.-R.and Lin, C.-J.}, | |
| Journal = {JMLR}, | |
| Year = {2008}, | |
| Number = {9}, | |
| Pages = {1871--1874} | |
| } | |
| @Article{fei2007learning, | |
| Title = {Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories}, | |
| Author = {Fei-Fei, Li and Fergus, Rob and Perona, Pietro}, | |
| Journal = {Computer vision and Image understanding}, | |
| Year = {2007}, | |
| Number = {1}, | |
| Pages = {59--70}, | |
| Volume = {106}, | |
| Publisher = {Elsevier} | |
| } | |
| @Article{felzenswalbGMR2010tpami, | |
| Title = {Object Detection with Discriminatively Trained Part-Based Models}, | |
| Author = {Felzenszwalb, P.F. and Girshick, R.B. and McAllester, D. and Ramanan, D.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2010}, | |
| Month = {Sept}, | |
| Number = {9}, | |
| Pages = {1627--1645}, | |
| Volume = {32}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @Article{fernandoTT15prl, | |
| Title = {Joint cross-domain classification and subspace learning for unsupervised adaptation}, | |
| Author = {Fernando, B. and Tommasi, T. and Tuytelaarsc, T.}, | |
| Journal = {Pattern Recogition Letters}, | |
| Year = {2015} | |
| } | |
| @Article{fouadTRS2013tnnls, | |
| Title = {Incorporating Privileged Information Through Metric Learning}, | |
| Author = {Fouad, S. and Tino, P. and Raychaudhury, S. and Schneider, P.}, | |
| Journal = {IEEE Trans. on Neural Networks and Learning Systems}, | |
| Year = {2013}, | |
| Month = {July}, | |
| Number = {7}, | |
| Pages = {1086--1098}, | |
| Volume = {24}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @Article{ganin2014unsupervised, | |
| Title = {Unsupervised domain adaptation by backpropagation}, | |
| Author = {Ganin, Yaroslav and Lempitsky, Victor}, | |
| Journal = {arXiv preprint arXiv:1409.7495}, | |
| Year = {2014} | |
| } | |
| @Article{ganin2016domain, | |
| Title = {Domain-adversarial training of neural networks}, | |
| Author = {Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, Fran{\c{c}}ois and Marchand, Mario and Lempitsky, Victor}, | |
| Journal = {Journal of Machine Learning Research}, | |
| Year = {2016}, | |
| Number = {59}, | |
| Pages = {1--35}, | |
| Volume = {17} | |
| } | |
| @Article{Gatys2016, | |
| Title = {{Image style transfer using convolutional neural networks}}, | |
| Author = {Gatys, Leon A and Ecker, Alexander S and Bethge, Matthias}, | |
| Journal = {The IEEE conference on computer vision and pattern recognition}, | |
| Year = {2016}, | |
| Pages = {2414--2423}, | |
| Abstract = {Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic in-formation and, thus, allow to separate image content from style. Here we use image representations derived from Con-volutional Neural Networks optimised for object recogni-tion, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can sep-arate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an ar-bitrary photograph with the appearance of numerous well-known artworks. Our results provide new insights into the deep image representations learned by Convolutional Neu-ral Networks and demonstrate their potential for high level image synthesis and manipulation.}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1505.07376}, | |
| Doi = {10.1109/CVPR.2016.265}, | |
| Eprint = {1505.07376}, | |
| File = {:data/mendeley/Gatys{\_}Image{\_}Style{\_}Transfer{\_}CVPR{\_}2016{\_}paper.pdf:pdf}, | |
| ISBN = {9781467388511}, | |
| ISSN = {10636919}, | |
| Mendeley-groups = {ganthing}, | |
| Pmid = {15430064963552939126} | |
| } | |
| @Article{Georghiades2001illumination, | |
| Title = {From few to many: illumination cone models for face recognition under variable lighting and pose}, | |
| Author = {Georghiades, A.S. and Belhumeur, P.N. and Kriegman, D.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2001}, | |
| Number = {6}, | |
| Pages = {643-660}, | |
| Volume = {23}, | |
| Doi = {10.1109/34.927464}, | |
| ISSN = {0162-8828}, | |
| Keywords = {albedo;computer vision;face recognition;image reconstruction;image representation;learning systems;lighting;rendering (computer graphics);albedo;appearance-based method;computer vision;generative models;human face recognition;illumination cone;image reconstruction;image representation;learning systems;lighting;pose modeling;rendering;Face recognition;Humans;Image recognition;Image reconstruction;Lighting;Performance evaluation;Rendering (computer graphics);Shape;Testing;Vectors} | |
| } | |
| @Article{ghifary2016scatter, | |
| Title = {Scatter component analysis: A unified framework for domain adaptation and domain generalization}, | |
| Author = {Ghifary, Muhammad and Balduzzi, David and Kleijn, W Bastiaan and Zhang, Mengjie}, | |
| Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, | |
| Year = {2017}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{goldfarbMS2013mp, | |
| Title = {Fast alternating linearization methods for minimizing the sum of two convex functions}, | |
| Author = {Goldfarb, D. and Ma, S. and Scheinberg, K.}, | |
| Journal = {Mathematical Programming}, | |
| Year = {2013}, | |
| Number = {1--2}, | |
| Pages = {349--382}, | |
| Volume = {141}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @Article{goodfellow2016nips, | |
| Title = {NIPS 2016 tutorial: Generative adversarial networks}, | |
| Author = {Goodfellow, Ian}, | |
| Journal = {arXiv preprint arXiv:1701.00160}, | |
| Year = {2016} | |
| } | |
| @Article{gretton2009covariate, | |
| Title = {Covariate shift by kernel mean matching}, | |
| Author = {Gretton, Arthur and Smola, Alexander J and Huang, Jiayuan and Schmittfull, Marcel and Borgwardt, Karsten M and Sch{\"o}lkopf, Bernhard}, | |
| Year = {2009}, | |
| Publisher = {MIT press} | |
| } | |
| @Article{Guangcan2013robust, | |
| Title = {Robust Recovery of Subspace Structures by Low-Rank Representation}, | |
| Author = {Guangcan Liu and Zhouchen Lin and Shuicheng Yan and Ju Sun and Yong Yu and Yi Ma}, | |
| Journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, | |
| Year = {2013}, | |
| Month = {Jan}, | |
| Number = {1}, | |
| Pages = {171-184}, | |
| Volume = {35}, | |
| Doi = {10.1109/TPAMI.2012.88}, | |
| ISSN = {0162-8828}, | |
| Keywords = {convex programming;pattern clustering;sparse matrices;LRR;convex program;data sample vectors;dictionary;low-rank representation;objective function;outlier removal;robust subspace structure recovery;row space;sparse error correction;subspace clustering problem;subspace membership;Data models;Dictionaries;Noise;Optimization;Polynomials;Robustness;Vectors;Low-rank representation;outlier detection;segmentation;subspace clustering;Algorithms;Artificial Intelligence;Computer Simulation;Models, Theoretical;Pattern Recognition, Automated;Signal Processing, Computer-Assisted} | |
| } | |
| @Article{gulccehre2016knowledge, | |
| Title = {Knowledge matters: Importance of prior information for optimization}, | |
| Author = {G{\"u}l{\c{c}}ehre, {\c{C}}a{\u{g}}lar and Bengio, Yoshua}, | |
| Journal = {Journal of Machine Learning Research}, | |
| Year = {2016}, | |
| Number = {8}, | |
| Pages = {1--32}, | |
| Volume = {17} | |
| } | |
| @Article{hardoon2004, | |
| Title = {Canonical Correlation Analysis: An Overview with Application to Learning Methods}, | |
| Author = {Hardoon, D. R. and Szedmak, S. and Shawe-Taylor, J.}, | |
| Journal = {Neural Computation}, | |
| Year = {2004}, | |
| Pages = {2639�2664}, | |
| Volume = {16}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.07.17} | |
| } | |
| @Article{hastieT96TPAMI, | |
| Title = {Discriminant adaptive nearest neighbor classification}, | |
| Author = {Hastie, T. and Tibshirani, R.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {1996}, | |
| Month = {Jun}, | |
| Number = {6}, | |
| Pages = {607-616}, | |
| Volume = {18}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.11.13} | |
| } | |
| @Article{Hertzmann2001, | |
| Title = {{Image analogies}}, | |
| Author = {Hertzmann, Aaron and Jacobs, Charles E. and Oliver, Nuria and Curless, Brian and Salesin, David H.}, | |
| Journal = {Proceedings of the 28th annual conference on Computer graphics and interactive techniques - SIGGRAPH '01}, | |
| Year = {2001}, | |
| Number = {August}, | |
| Pages = {327--340}, | |
| Abstract = {This paper describes a new framework for processing images by example, called image analogies. The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a filtered version of the other, is presented as training data; and an application phase, in which the learned filter is applied to some new target image in order to create an analogous filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of image filter effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are texturized with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1705.01088}, | |
| Doi = {10.1145/383259.383295}, | |
| Eprint = {1705.01088}, | |
| File = {:data/mendeley/p327-hertzmann.pdf:pdf}, | |
| ISBN = {158113374X}, | |
| Mendeley-groups = {ganthing}, | |
| Url = {http://portal.acm.org/citation.cfm?doid=383259.383295} | |
| } | |
| @Article{Hoffman2017, | |
| Title = {{CyCADA: Cycle-Consistent Adversarial Domain Adaptation}}, | |
| Author = {Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei A. and Darrell, Trevor}, | |
| Year = {2017}, | |
| Pages = {1--15}, | |
| Abstract = {Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1711.03213}, | |
| Eprint = {1711.03213}, | |
| File = {:data/mendeley/1711.03213.pdf:pdf}, | |
| Mendeley-groups = {ganthing}, | |
| Url = {http://arxiv.org/abs/1711.03213} | |
| } | |
| @Article{hoffman2017cycada, | |
| Title = {CyCADA: Cycle-Consistent Adversarial Domain Adaptation}, | |
| Author = {Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei A and Darrell, Trevor}, | |
| Journal = {arXiv preprint arXiv:1711.03213}, | |
| Year = {2017} | |
| } | |
| @Article{hsu2002comparison, | |
| Title = {A comparison of methods for multiclass support vector machines}, | |
| Author = {Hsu, Chih-Wei and Lin, Chih-Jen}, | |
| Journal = {IEEE transactions on Neural Networks}, | |
| Year = {2002}, | |
| Number = {2}, | |
| Pages = {415--425}, | |
| Volume = {13}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{huang2011generalized, | |
| Title = {Generalized sparse metric learning with relative comparisons}, | |
| Author = {Huang, Kaizhu and Ying, Yiming and Campbell, Colin}, | |
| Journal = {Knowledge and Information Systems}, | |
| Year = {2011}, | |
| Number = {1}, | |
| Pages = {25--45}, | |
| Volume = {28}, | |
| Publisher = {Springer} | |
| } | |
| @Article{hull1994database, | |
| Title = {A database for handwritten text recognition research}, | |
| Author = {Hull, Jonathan J.}, | |
| Journal = {IEEE Transactions on pattern analysis and machine intelligence}, | |
| Year = {1994}, | |
| Number = {5}, | |
| Pages = {550--554}, | |
| Volume = {16}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{imagenet2015, | |
| Title = {{ImageNet Large Scale Visual Recognition Challenge}}, | |
| Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei}, | |
| Journal = {IJCV}, | |
| Year = {2015}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.22} | |
| } | |
| @Article{isola2016image, | |
| Title = {Image-to-image translation with conditional adversarial networks}, | |
| Author = {Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, | |
| Journal = {arXiv preprint arXiv:1611.07004}, | |
| Year = {2016} | |
| } | |
| @Article{Isola2017, | |
| Title = {{Image-to-Image Translation with Conditional Adversarial Networks}}, | |
| Author = {Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A.}, | |
| Journal = {CVPR}, | |
| Year = {2017}, | |
| Pages = {1125--1134}, | |
| Abstract = {We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1611.07004}, | |
| Doi = {10.1109/CVPR.2017.632}, | |
| Eprint = {1611.07004}, | |
| File = {:data/mendeley/Isola{\_}Image-To-Image{\_}Translation{\_}With{\_}CVPR{\_}2017{\_}paper.pdf:pdf}, | |
| ISBN = {978-1-5386-0457-1}, | |
| ISSN = {08883270}, | |
| Mendeley-groups = {ganthing}, | |
| Pmid = {14706220}, | |
| Url = {http://arxiv.org/abs/1611.07004} | |
| } | |
| @Article{Johnson2016, | |
| Title = {{Perceptual Losses for Real-Time Style Transfer and Super-Resolution}}, | |
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| Abstract = {We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. Par- allel work has shown that high-quality images can be generated by defin- ing and optimizing perceptual loss functions based on high-level features extracted from pretrained networks. We combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image transformation tasks. We show results on image style transfer, where a feed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time. Com- pared to the optimization-based method, our network gives similar quali- tative results but is three orders of magnitude faster.We also experiment with single-image super-resolution, where replacing a per-pixel loss with a perceptual loss gives visually pleasing results. Keywords:}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1612.02155}, | |
| Doi = {10.1007/978-3-319-46475-6}, | |
| Eprint = {1612.02155}, | |
| File = {:data/mendeley/978-3-319-46475-6{\_}43.pdf:pdf}, | |
| ISBN = {9783319464756}, | |
| ISSN = {0302-9743}, | |
| Keywords = {deep learning,style transfer,super-resolution}, | |
| Mendeley-groups = {ganthing}, | |
| Pmid = {4520227}, | |
| Url = {http://arxiv.org/abs/1612.02155} | |
| } | |
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| Abstract = {We propose the coupled generative adversarial network (CoGAN) framework for generating pairs of corresponding images in two different domains. It consists of a pair of generative adversarial networks, each responsible for generating images in one domain. We show that by enforcing a simple weight-sharing constraint, the CoGAN learns to generate pairs of corresponding images without existence of any pairs of corresponding images in the two domains in the training set. In other words, the CoGAN learns a joint distribution of images in the two domains from images drawn separately from the marginal distributions of the individual domains. This is in contrast to the existing multi-modal generative models, which require corresponding images for training. We apply the CoGAN to several pair image generation tasks. For each task, the GoGAN learns to generate convincing pairs of corresponding images. We further demonstrate the applications of the CoGAN framework for the domain adaptation and cross-domain image generation tasks.}, | |
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| File = {:data/mendeley/6544-coupled-generative-adversarial-networks.pdf:pdf}, | |
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| Abstract = {This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.}, | |
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| Abstract = {We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.}, | |
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| Doi = {10.1109/CVPR.2017.106}, | |
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| Author = {Torfi, Amirsina and Nasrabadi, Nasser M and Dawson, Jeremy}, | |
| Journal = {arXiv preprint arXiv:1705.09422}, | |
| Year = {2017} | |
| } | |
| @Article{tzeng2014deep, | |
| Title = {Deep domain confusion: Maximizing for domain invariance}, | |
| Author = {Tzeng, Eric and Hoffman, Judy and Zhang, Ning and Saenko, Kate and Darrell, Trevor}, | |
| Journal = {arXiv preprint arXiv:1412.3474}, | |
| Year = {2014} | |
| } | |
| @Article{vapnikV2009, | |
| Title = {A new learning paradigm: Learning using privileged information}, | |
| Author = {Vapnik, V. and Vashist, A.}, | |
| Journal = {Neural Networks}, | |
| Year = {2009}, | |
| Number = {5--6}, | |
| Pages = {544--557}, | |
| Volume = {22}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.15} | |
| } | |
| @Article{vidal2013low, | |
| Title = {Low rank subspace clustering (LRSC)}, | |
| Author = {Vidal, Ren{\'e} and Favaro, Paolo}, | |
| Journal = {Pattern Recognition Letters}, | |
| Year = {2013}, | |
| Publisher = {Elsevier} | |
| } | |
| @Article{wagnerWGZMM12IEEEtpami, | |
| Title = {Toward a Practical Face Recognition System: {R}obust Alignment and Illumination by Sparse Representation}, | |
| Author = {Wagner, A. and Wright, J. and Ganesh, A. and Zhou, Z. and Mobahi, H. and Ma, Y.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2012}, | |
| Number = {2}, | |
| Pages = {372-386}, | |
| Volume = {34}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.06.01} | |
| } | |
| @Article{wang2016relative, | |
| Title = {Relative attribute SVM+ learning for age estimation}, | |
| Author = {Wang, Shengzheng and Tao, Dacheng and Yang, Jie}, | |
| Journal = {IEEE transactions on cybernetics}, | |
| Year = {2016}, | |
| Number = {3}, | |
| Pages = {827--839}, | |
| Volume = {46}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{weinberger2009distance, | |
| Title = {Distance metric learning for large margin nearest neighbor classification}, | |
| Author = {Weinberger, Kilian Q and Saul, Lawrence K}, | |
| Journal = {The Journal of Machine Learning Research}, | |
| Year = {2009}, | |
| Pages = {207--244}, | |
| Volume = {10}, | |
| Publisher = {JMLR. org} | |
| } | |
| @Article{wolfHT11tpami, | |
| Title = {Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics}, | |
| Author = {L. Wolf and T. Hassner and Y. Taigman}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2011}, | |
| Number = {10}, | |
| Pages = {1978-1990}, | |
| Volume = {33} | |
| } | |
| @Article{wright2009robust2, | |
| Title = {Robust Face Recognition via Sparse Representation}, | |
| Author = {Wright, J. and Yang, A.Y. and Ganesh, A. and Sastry, S.S. and Yi Ma}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2009}, | |
| Number = {2}, | |
| Pages = {210-227}, | |
| Volume = {31}, | |
| Doi = {10.1109/TPAMI.2008.79}, | |
| ISSN = {0162-8828}, | |
| Keywords = {face recognition;feature extraction;lightning;object recognition;random processes;regression analysis;signal representation;Laplacianfaces;downsampled images;eigenfaces;feature extraction;illumination;image-based object recognition;multiple linear regression model;occlusion;random projections;robust face recognition;sparse signal representation;Classification algorithms;Face recognition;Feature extraction;Humans;Image recognition;Lighting;Linear regression;Object recognition;Robustness;Signal representations;Classifier design and evaluation;Face and gesture recognition;Face recognition;Feature evaluation and selection;Occlusion;Outlier rejection;Spare representation;compressed sensing;ell^{1}--minimization;feature extraction;occlusion and corruption;sparse representation;validation and outlier rejection.;Algorithms;Artificial Intelligence;Biometry;Cluster Analysis;Face;Humans;Image Enhancement;Image Interpretation, Computer-Assisted;Pattern Recognition, Automated;Reproducibility of Results;Sensitivity and Specificity;Subtraction Technique} | |
| } | |
| @Article{wulfmeier2016incorporating, | |
| Title = {Incorporating Human Domain Knowledge into Large Scale Cost Function Learning}, | |
| Author = {Wulfmeier, Markus and Rao, Dushyant and Posner, Ingmar}, | |
| Journal = {arXiv preprint arXiv:1612.04318}, | |
| Year = {2016} | |
| } | |
| @Article{xu2013survey, | |
| Title = {A survey on multi-view learning}, | |
| Author = {Xu, Chang and Tao, Dacheng and Xu, Chao}, | |
| Journal = {arXiv preprint arXiv:1304.5634}, | |
| Year = {2013} | |
| } | |
| @Article{xuLX2015tnnls, | |
| Title = {Distance Metric Learning Using Privileged Information for Face Verification and Person Re-Identification}, | |
| Author = {Xu, X. and Li, W. and Xu, D.}, | |
| Journal = {IEEE Trans. on Neural Networks and Learning Systems}, | |
| Year = {2015}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @Article{xuTX2014tpami, | |
| Title = {Large-Margin Multi-View Information Bottleneck}, | |
| Author = {Xu, C. and Tao, D. and Xu, C.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2014}, | |
| Number = {8}, | |
| Pages = {1559-1572}, | |
| Volume = {36}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @Article{yang2018person, | |
| Title = {Person Re-Identification With Metric Learning Using Privileged Information}, | |
| Author = {Yang, Xun and Wang, Meng and Tao, Dacheng}, | |
| Journal = {IEEE Transactions on Image Processing}, | |
| Year = {2018}, | |
| Number = {2}, | |
| Pages = {791--805}, | |
| Volume = {27}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{yangZFY04IEEEtpami, | |
| Title = {Two-dimensional {PCA}: a new approach to appearance-based face representation and recognition}, | |
| Author = {Yang, J. and Zhang, D. and Frangi, A. F. and Yang, J.Y.}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2004}, | |
| Number = {1}, | |
| Pages = {131-137}, | |
| Volume = {26}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.06.05} | |
| } | |
| @Article{yeung2007kernel, | |
| Title = {A kernel approach for semisupervised metric learning}, | |
| Author = {Yeung, Dit-Yan and Chang, Hong}, | |
| Journal = {Neural Networks, IEEE Transactions on}, | |
| Year = {2007}, | |
| Number = {1}, | |
| Pages = {141--149}, | |
| Volume = {18}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{Yi2017, | |
| Title = {{DualGAN: Unsupervised Dual Learning for Image-to-Image Translation}}, | |
| Author = {Yi, Zili and Zhang, Hao and Tan, Ping and Gong, Minglun}, | |
| Journal = {Proceedings of the IEEE International Conference on Computer Vision}, | |
| Year = {2017}, | |
| Pages = {2868--2876}, | |
| Volume = {2017-October}, | |
| Abstract = {Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractical, and large quantities of data may not always be available. Inspired by dual learning from natural language translation, we develop a novel dual-GAN mechanism, which enables image translators to be trained from two sets of unlabeled images from two domains. In our architecture, the primal GAN learns to translate images from domain U to those in domain V, while the dual GAN learns to invert the task. The closed loop made by the primal and dual tasks allows images from either domain to be translated and then reconstructed. Hence a loss function that accounts for the reconstruction error of images can be used to train the translators. Experiments on multiple image translation tasks with unlabeled data show considerable performance gain of DualGAN over a single GAN. For some tasks, DualGAN can even achieve comparable or slightly better results than conditional GAN trained on fully labeled data.}, | |
| Archiveprefix = {arXiv}, | |
| Arxivid = {1704.02510}, | |
| Doi = {10.1109/ICCV.2017.310}, | |
| Eprint = {1704.02510}, | |
| File = {:data/mendeley/Yi{\_}DualGAN{\_}Unsupervised{\_}Dual{\_}ICCV{\_}2017{\_}paper.pdf:pdf}, | |
| ISBN = {9781538610329}, | |
| ISSN = {15505499}, | |
| Mendeley-groups = {ganthing,ganthing/translation} | |
| } | |
| @Article{Yigang2012RASL, | |
| Title = {RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images}, | |
| Author = {Yigang Peng and Ganesh, A. and Wright, J. and Wenli Xu and Yi Ma}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2012}, | |
| Month = {Nov}, | |
| Number = {11}, | |
| Pages = {2233-2246}, | |
| Volume = {34}, | |
| Doi = {10.1109/TPAMI.2011.282}, | |
| ISSN = {0162-8828} | |
| } | |
| @Article{you2017privileged, | |
| Title = {Privileged Multi-label Learning}, | |
| Author = {You, Shan and Xu, Chang and Wang, Yunhe and Xu, Chao and Tao, Dacheng}, | |
| Journal = {arXiv preprint arXiv:1701.07194}, | |
| Year = {2017} | |
| } | |
| @Article{Zhang2012TILT, | |
| Title = {TILT: Transform Invariant Low-Rank Textures}, | |
| Author = {Zhang, Zhengdong and Ganesh, Arvind and Liang, Xiao and Ma, Yi}, | |
| Journal = {International Journal of Computer Vision}, | |
| Year = {2012}, | |
| Number = {1}, | |
| Pages = {1-24}, | |
| Volume = {99}, | |
| Doi = {10.1007/s11263-012-0515-x}, | |
| ISSN = {0920-5691}, | |
| Keywords = {Transform invariant; Low-rank texture; Sparse errors; Robust PCA; Rank minimization; Image rectification; Shape from texture; Symmetry}, | |
| Language = {English}, | |
| Publisher = {Springer US}, | |
| Url = {http://dx.doi.org/10.1007/s11263-012-0515-x} | |
| } | |
| @Article{zhang2016stackgan, | |
| Title = {Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks}, | |
| Author = {Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Huang, Xiaolei and Wang, Xiaogang and Metaxas, Dimitris}, | |
| Journal = {arXiv preprint arXiv:1612.03242}, | |
| Year = {2016} | |
| } | |
| @Article{zhao2017multi, | |
| Title = {Multi-view learning overview: Recent progress and new challenges}, | |
| Author = {Zhao, Jing and Xie, Xijiong and Xu, Xin and Sun, Shiliang}, | |
| Journal = {Information Fusion}, | |
| Year = {2017}, | |
| Pages = {43--54}, | |
| Volume = {38}, | |
| Publisher = {Elsevier} | |
| } | |
| @Article{zheng2013reidentification, | |
| Title = {Reidentification by relative distance comparison}, | |
| Author = {Zheng, Wei-Shi and Gong, Shaogang and Xiang, Tao}, | |
| Journal = {IEEE TPAMI}, | |
| Year = {2013}, | |
| Number = {3}, | |
| Pages = {653--668}, | |
| Volume = {35}, | |
| Publisher = {IEEE} | |
| } | |
| @Article{Zhu2017, | |
| author = {Zhu, Jun Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A.}, | |
| title = {{Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks}}, | |
| journal = {IEEE International Conference on Computer Vision (ICCV)}, | |
| year = {2017}, | |
| volume = {2017-Octob}, | |
| pages = {2242--2251}, | |
| issn = {15505499}, | |
| abstract = {Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain {\$}X{\$} to a target domain {\$}Y{\$} in the absence of paired examples. Our goal is to learn a mapping {\$}G: X \backslashrightarrow Y{\$} such that the distribution of images from {\$}G(X){\$} is indistinguishable from the distribution {\$}Y{\$} using an adversarial loss. Because this mapping is highly under-constrained, we couple it with an inverse mapping {\$}F: Y \backslashrightarrow X{\$} and introduce a cycle consistency loss to push {\$}F(G(X)) \backslashapprox X{\$} (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc. Quantitative comparisons against several prior methods demonstrate the superiority of our approach.}, | |
| annote = {while also unpaired our approach maintains semantic character of the images by furthering the ideas of f-constancy and the related cogan loss}, | |
| archiveprefix = {arXiv}, | |
| arxivid = {1703.10593}, | |
| doi = {10.1109/ICCV.2017.244}, | |
| eprint = {1703.10593}, | |
| file = {:data/mendeley/1703.10593.pdf:pdf}, | |
| isbn = {9781538610329}, | |
| mendeley-groups = {ganthing}, | |
| } | |
| @Book{coverT91book, | |
| Title = {Elements of Information Theory}, | |
| Author = {Cover, T. M. and Thomas, J. A.}, | |
| Publisher = {Wiley and Sons, Inc.}, | |
| Year = {1991}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.18} | |
| } | |
| @Book{sun2012computer, | |
| Title = {Computer vision technology in the food and beverage industries}, | |
| Author = {Sun, Da-Wen}, | |
| Publisher = {Elsevier}, | |
| Year = {2012} | |
| } | |
| @Book{vapnik2013nature, | |
| Title = {The nature of statistical learning theory}, | |
| Author = {Vapnik, Vladimir}, | |
| Publisher = {Springer science \& business media}, | |
| Year = {2013} | |
| } | |
| @InBook{Siyahjani2013, | |
| Title = {Learning a Context Aware Dictionary for Sparse Representation}, | |
| Author = {Siyahjani, Farzad | |
| and Doretto, Gianfranco}, | |
| Pages = {228--241}, | |
| Publisher = {Springer Berlin Heidelberg}, | |
| Year = {2013}, | |
| Address = {Berlin, Heidelberg}, | |
| Booktitle = {Computer Vision -- ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part II} | |
| } | |
| @InCollection{attributes2012, | |
| author = {Layne, Ryan and Hospedales, TimothyM. and Gong, Shaogang}, | |
| title = {Towards Person Identification and Re-identification with Attributes}, | |
| booktitle = {Computer Vision ECCV 2012. Workshops and Demonstrations}, | |
| publisher = {Springer Berlin Heidelberg}, | |
| year = {2012}, | |
| editor = {Fusiello, Andrea and Murino, Vittorio and Cucchiara, Rita}, | |
| volume = {7583}, | |
| series = {Lecture Notes in Computer Science}, | |
| pages = {402-412}, | |
| isbn = {978-3-642-33862-5}, | |
| doi = {10.1007/978-3-642-33863-2_40}, | |
| url = {http://dx.doi.org/10.1007/978-3-642-33863-2_40}, | |
| } | |
| @InCollection{gray_2007, | |
| Title = {Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features}, | |
| Author = {Gray, Douglas and Tao, Hai}, | |
| Booktitle = {Computer Vision – ECCV 2008}, | |
| Publisher = {Springer Berlin Heidelberg}, | |
| Year = {2008}, | |
| Editor = {Forsyth, David and Torr, Philip and Zisserman, Andrew}, | |
| Pages = {262-275}, | |
| Series = {Lecture Notes in Computer Science}, | |
| Volume = {5302}, | |
| Doi = {10.1007/978-3-540-88682-2_21}, | |
| ISBN = {978-3-540-88681-5}, | |
| Url = {http://dx.doi.org/10.1007/978-3-540-88682-2_21} | |
| } | |
| @InCollection{hirzer2012relaxed, | |
| Title = {Relaxed pairwise learned metric for person re-identification}, | |
| Author = {Hirzer, Martin and Roth, Peter M and K{\"o}stinger, Martin and Bischof, Horst}, | |
| Booktitle = {Computer Vision--ECCV 2012}, | |
| Publisher = {Springer}, | |
| Year = {2012}, | |
| Pages = {780--793} | |
| } | |
| @InCollection{nguyen2011cosine, | |
| Title = {Cosine similarity metric learning for face verification}, | |
| Author = {Nguyen, Hieu V and Bai, Li}, | |
| Booktitle = {Computer Vision--ACCV 2010}, | |
| Publisher = {Springer}, | |
| Year = {2011}, | |
| Pages = {709--720} | |
| } | |
| @InCollection{NIPS2005_388, | |
| Title = {Metric Learning by Collapsing Classes}, | |
| Author = {Amir Globerson and Sam Roweis}, | |
| Booktitle = {Advances in Neural Information Processing Systems 18}, | |
| Publisher = {MIT Press}, | |
| Year = {2006}, | |
| Address = {Cambridge, MA}, | |
| Editor = {Y. Weiss and B. Sch\"{o}lkopf and J. Platt}, | |
| Pages = {451--458} | |
| } | |
| @InProceedings{Goodfellow2014, | |
| author = {Goodfellow, I. and Pouget-Abadie, J. and Mirza, M. and Xu, B. and Warde-Farley, D. and Ozair, S. and Courville, A. and Bengio, Y.}, | |
| title = {Generative Adversarial Nets}, | |
| booktitle = {Advances in Neural Information Processing Systems (NIPS)}, | |
| year = {2014}, | |
| pages = {2672--2680}, | |
| url = {http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf}, | |
| } | |
| @InCollection{ponce2006dataset, | |
| Title = {Dataset issues in object recognition}, | |
| Author = {Ponce, Jean and Berg, Tamara L and Everingham, Mark and Forsyth, David A and Hebert, Martial and Lazebnik, Svetlana and Marszalek, Marcin and Schmid, Cordelia and Russell, Bryan C and Torralba, Antonio and others}, | |
| Booktitle = {Toward category-level object recognition}, | |
| Publisher = {Springer}, | |
| Year = {2006}, | |
| Pages = {29--48} | |
| } | |
| @InCollection{svm-light, | |
| Title = {Making large-Scale {SVM} Learning Practical}, | |
| Author = {Joachims, T.}, | |
| Booktitle = {Advances in Kernel Methods - Support Vector Learning}, | |
| Publisher = {MIT Press}, | |
| Year = {1999}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.22} | |
| } | |
| @InProceedings{kuehneJGPS2011iccv, | |
| Title = {{HMDB}: a large video database for human motion recognition}, | |
| Author = {Kuehne, H. and Jhuang, H. and Garrote, E. and Poggio, T. and Serre, T.}, | |
| Booktitle = {IEEE ICCV}, | |
| Year = {2011}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.22} | |
| } | |
| @InProceedings{4409019, | |
| Title = {Shape and Appearance Context Modeling}, | |
| Author = {Xiaogang Wang and Doretto, G. and Sebastian, T. and Rittscher, J. and Tu, P.}, | |
| Booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on}, | |
| Year = {2007}, | |
| Month = {Oct}, | |
| Pages = {1-8}, | |
| Doi = {10.1109/ICCV.2007.4409019}, | |
| ISSN = {1550-5499}, | |
| Keywords = {image processing;matrix algebra;appearance context modeling;image regions;integral histogram;integral image;occurrence matrices;shape context modeling;spatial distribution;Cameras;Computational complexity;Computer vision;Context modeling;Deformable models;Dictionaries;Histograms;Lighting;Robustness;Shape} | |
| } | |
| @InProceedings{4635689, | |
| Title = {Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences}, | |
| Author = {Hamdoun, O. and Moutarde, F. and Stanciulescu, B. and Steux, B.}, | |
| Booktitle = {Distributed Smart Cameras, 2008. ICDSC 2008. Second ACM/IEEE International Conference on}, | |
| Year = {2008}, | |
| Month = {Sept}, | |
| Pages = {1-6}, | |
| Doi = {10.1109/ICDSC.2008.4635689}, | |
| Keywords = {image matching;image sensors;image sequences;sensor fusion;video surveillance;interest point descriptors;logarithmic dependence;matching method;multi-camera surveillance system;person re-identification;person tracking;short video sequences;time-spaced images;Biometrics;Cameras;Clustering algorithms;Detectors;Face recognition;Laboratories;Performance evaluation;Robots;Surveillance;Video sequences;Video-surveillance;camera networks;interest points;person identification and tracking;re-identification} | |
| } | |
| @InProceedings{ali18, | |
| Title = {Fingerprint Distortion Rectification using Deep Convolutional Neural Networks}, | |
| Author = {Dabouei, Ali and Kazemi, Hadi and Iranmanesh, Mehdi and Nasrabadi, Nasser M.}, | |
| Booktitle = {Biometrics (ICB), 2018 International Conference on}, | |
| Year = {2018}, | |
| Organization = {IEEE} | |
| } | |
| @InProceedings{aytar2011tabula, | |
| Title = {Tabula rasa: Model transfer for object category detection}, | |
| Author = {Aytar, Yusuf and Zisserman, Andrew}, | |
| Booktitle = {Computer Vision (ICCV), 2011 IEEE International Conference on}, | |
| Year = {2011}, | |
| Organization = {IEEE}, | |
| Pages = {2252--2259} | |
| } | |
| @InProceedings{baktashmotlaghHLS13iccv, | |
| Title = {Unsupervised Domain Adaptation by Domain Invariant Projection}, | |
| Author = {Baktashmotlagh, M. and Harandi, M. T. and Lovell, B. C. and Salzmann, M.}, | |
| Booktitle = {IEEE ICCV}, | |
| Year = {2013}, | |
| Pages = {769-776} | |
| } | |
| @InProceedings{BaktashmotlaghHLS14cvpr, | |
| Title = {Domain Adaptation on the Statistical Manifold}, | |
| Author = {Baktashmotlagh, M. and Harandi, M. T. and Lovell, B. C. and Salzmann, M.}, | |
| Booktitle = {CVPR}, | |
| Year = {2014}, | |
| Pages = {2481-2488} | |
| } | |
| @InProceedings{basuBM2004kdd, | |
| Title = {A Probabilistic Framework for Semi-Supervised Clustering}, | |
| Author = {Basu, S. and Bilenko, M. and Mooney, R. .J.}, | |
| Booktitle = {KDD}, | |
| Year = {2004}, | |
| Pages = {59--68}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{becker2013non, | |
| Title = {Non-linear domain adaptation with boosting}, | |
| Author = {Becker, Carlos J and Christoudias, Christos M and Fua, Pascal}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
| Year = {2013}, | |
| Pages = {485--493} | |
| } | |
| @InProceedings{bergamo2010exploiting, | |
| Title = {Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach}, | |
| Author = {Bergamo, Alessandro and Torresani, Lorenzo}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
| Year = {2010}, | |
| Pages = {181--189} | |
| } | |
| @InProceedings{bilenkoBM04icml, | |
| Title = {Integrating Constraints and Metric Learning in Semi-Supervised Clustering}, | |
| Author = {Bilenko, M. and Basu, S. and Mooney, R. J.}, | |
| Booktitle = {ICML}, | |
| Year = {2004}, | |
| Pages = {81--88}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.11.13} | |
| } | |
| @InProceedings{blanchard2011generalizing, | |
| Title = {Generalizing from several related classification tasks to a new unlabeled sample}, | |
| Author = {Blanchard, Gilles and Lee, Gyemin and Scott, Clayton}, | |
| Booktitle = {Advances in neural information processing systems}, | |
| Year = {2011}, | |
| Pages = {2178--2186} | |
| } | |
| @InProceedings{blitzer2006domain, | |
| Title = {Domain adaptation with structural correspondence learning}, | |
| Author = {Blitzer, John and McDonald, Ryan and Pereira, Fernando}, | |
| Booktitle = {Proceedings of the 2006 conference on empirical methods in natural language processing}, | |
| Year = {2006}, | |
| Organization = {Association for Computational Linguistics}, | |
| Pages = {120--128} | |
| } | |
| @InProceedings{blitzer2011domain, | |
| Title = {Domain adaptation with coupled subspaces}, | |
| Author = {Blitzer, John and Kakade, Sham and Foster, Dean}, | |
| Booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, | |
| Year = {2011}, | |
| Pages = {173--181} | |
| } | |
| @InProceedings{blumM1998colt, | |
| Title = {Combining labeled and unlabeled data with co-training}, | |
| Author = {Blum, A. and Mitchell, T.}, | |
| Booktitle = {COLT}, | |
| Year = {1998}, | |
| Pages = {92--100}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{bohneYGP14eccv, | |
| Title = {Large Margin Local Metric Learning}, | |
| Author = {Bohne, J. and Ying, Y. and Gentric, S. and Pontil, M.}, | |
| Booktitle = {ECCV}, | |
| Year = {2014}, | |
| Pages = {679--694}, | |
| Volume = {8690}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.11.13} | |
| } | |
| @InProceedings{boRFiros, | |
| Title = {Depth Kernel Descriptors for Object Recognition}, | |
| Author = {Bo, L. and Ren, X. and Fox, D.}, | |
| Booktitle = {IROS}, | |
| Year = {2011} | |
| } | |
| @InProceedings{bromley1994signature, | |
| Title = {Signature verification using a" siamese" time delay neural network}, | |
| Author = {Bromley, Jane and Guyon, Isabelle and LeCun, Yann and S{\"a}ckinger, Eduard and Shah, Roopak}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
| Year = {1994}, | |
| Pages = {737--744} | |
| } | |
| @InProceedings{chechikT2002nips, | |
| Title = {Extracting Relevant Structures with Side Information}, | |
| Author = {Chechik, G. and Tishby, N.}, | |
| Booktitle = {NIPS}, | |
| Year = {2002}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{chen2012low, | |
| Title = {Low-rank matrix recovery with structural incoherence for robust face recognition}, | |
| Author = {Chih-Fan Chen and Chia-Po Wei and Wang, Y.-C.F.}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2012}, | |
| Pages = {2618-2625}, | |
| Doi = {10.1109/CVPR.2012.6247981}, | |
| ISSN = {1063-6919}, | |
| Keywords = {approximation theory;eigenvalues and eigenfunctions;face recognition;image classification;image representation;matrix algebra;SRC method;disguise;eigenfaces;face image modeling;low-rank matrix approximation algorithm;low-rank matrix recovery;occlusion;public face database;robust face recognition;sparse error;sparse representation-based classification;structural incoherence;Face;Face recognition;Matrix decomposition;Principal component analysis;Robustness;Sparse matrices;Training} | |
| } | |
| @InProceedings{cheng2011iccv, | |
| Title = {Multi-task low-rank affinity pursuit for image segmentation}, | |
| Author = {Cheng, B. and Liu, G. and Wang, J. and Huang, Z. and Yan, S.}, | |
| Booktitle = {IEEE ICCV}, | |
| Year = {2011}, | |
| Pages = {2439-2446}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.03.07} | |
| } | |
| @InProceedings{cheng2011multi, | |
| Title = {Multi-task low-rank affinity pursuit for image segmentation}, | |
| Author = {Bin Cheng and Guangcan Liu and Jingdong Wang and Zhongyang Huang and Shuicheng Yan}, | |
| Booktitle = {IEEE ICCV}, | |
| Year = {2011}, | |
| Pages = {2439-2446}, | |
| Doi = {10.1109/ICCV.2011.6126528}, | |
| ISSN = {1550-5499}, | |
| Keywords = {convex programming;image fusion;image segmentation;matrix algebra;minimisation;l2;1-norm minimization problem;Berkeley segmentation dataset;MSRC dataset;augmented Lagrange multiplier method;collaborative image segmentation;constrained nuclear norm;convex;image affinity matrix;image feature fusion;image feature matrix;multitask low-rank affinity pursuit;region-based image segmentation;unified affinity matrix;Reliability;Silicon} | |
| } | |
| @InProceedings{chenLL2012accv, | |
| Title = {Boosting with Side Information}, | |
| Author = {Chen, J. and Liu, X. and Lyu, S.}, | |
| Booktitle = {ACCV}, | |
| Year = {2012}, | |
| Pages = {563--577}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{chenLX2014cvpr, | |
| Title = {Recognizing {RGB} Images by Learning from {RGB-D} Data}, | |
| Author = {Chen, L. and Li, W. and Xu, D.}, | |
| Booktitle = {CVPR}, | |
| Year = {2014}, | |
| Month = {June}, | |
| Pages = {1418-1425}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{chenXYT18eccv, | |
| Title = {Attention-GAN for Object Transfiguration in Wild Images}, | |
| Author = {Chen, X and Xu, C. and Yang, X. and Tao, D.}, | |
| Booktitle = {European Conference on Computer Vision (ECCV)}, | |
| Year = {2018} | |
| } | |
| @InProceedings{choi2010exploiting, | |
| Title = {Exploiting hierarchical context on a large database of object categories}, | |
| Author = {Choi, Myung Jin and Lim, Joseph J and Torralba, Antonio and Willsky, Alan S}, | |
| Booktitle = {Computer vision and pattern recognition (CVPR), 2010 IEEE conference on}, | |
| Year = {2010}, | |
| Organization = {IEEE}, | |
| Pages = {129--136} | |
| } | |
| @InProceedings{chopra2005learning, | |
| Title = {Learning a similarity metric discriminatively, with application to face verification}, | |
| Author = {Chopra, Sumit and Hadsell, Raia and LeCun, Yann}, | |
| Booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on}, | |
| Year = {2005}, | |
| Organization = {IEEE}, | |
| Pages = {539--546}, | |
| Volume = {1} | |
| } | |
| @InProceedings{Deep2012Cai, | |
| Title = {Deep Nonlinear Metric Learning with Independent Subspace Analysis for Face Verification}, | |
| Author = {Cai, Xinyuan and Wang, Chunheng and Xiao, Baihua and Chen, Xue and Zhou, Ji}, | |
| Booktitle = {Proceedings of the 20th ACM International Conference on Multimedia}, | |
| Year = {2012}, | |
| Address = {New York, NY, USA}, | |
| Pages = {749--752}, | |
| Publisher = {ACM}, | |
| Series = {MM '12}, | |
| Acmid = {2396303}, | |
| Doi = {10.1145/2393347.2396303}, | |
| ISBN = {978-1-4503-1089-5}, | |
| Keywords = {deep learning architecture, face verification, independent subspace analysis}, | |
| Location = {Nara, Japan}, | |
| Numpages = {4}, | |
| Url = {http://doi.acm.org/10.1145/2393347.2396303} | |
| } | |
| @InProceedings{dengHG13cvpr, | |
| Title = {In Defense of Sparsity Based Face Recognition}, | |
| Author = {Deng, W. and Hu, J. and Guo, J.}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2013}, | |
| Pages = {399-406}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.06.01} | |
| } | |
| @InProceedings{Discriminative2014Hu, | |
| Title = {Discriminative Deep Metric Learning for Face Verification in the Wild}, | |
| Author = {Hu, Junlin and Lu, Jiwen and Tan, Yap-Peng}, | |
| Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on}, | |
| Year = {2014}, | |
| Month = {June}, | |
| Pages = {1875-1882}, | |
| Doi = {10.1109/CVPR.2014.242}, | |
| Keywords = {Face;Feature extraction;Learning systems;Measurement;Training;Vectors;Videos;Deep Learning;Face Verification;Metric Learning} | |
| } | |
| @InProceedings{Donahue13decaf, | |
| Title = {{DeCAF:} A deep convolutional activation feature for generic visual recognition}, | |
| Author = {Donahue, J. and Jia, Y. and Vinyals, O. and Hoffman, J. and Zhang, N. and Tzeng, E. and Darrell, T.}, | |
| Booktitle = {arXiv:1310.1531}, | |
| Year = {2013} | |
| } | |
| @InProceedings{donahueG2011iccv, | |
| Title = {Annotator rationales for visual recognition}, | |
| Author = {Donahue, J. and Grauman, K.}, | |
| Booktitle = {ICCV}, | |
| Year = {2011}, | |
| Month = {Nov}, | |
| Pages = {1395--1402}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{duan2009domain, | |
| Title = {Domain transfer svm for video concept detection}, | |
| Author = {Duan, Lixin and Tsang, Ivor W and Xu, Dong and Maybank, Stephen J}, | |
| Booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on}, | |
| Year = {2009}, | |
| Organization = {IEEE}, | |
| Pages = {1375--1381} | |
| } | |
| @InProceedings{DuanICML2012, | |
| Title = {Learning with Augmented Features for Heterogeneous Domain Adaptation}, | |
| Author = {Lixin Duan and Dong Xu and Ivor W. Tsang}, | |
| Booktitle = {Proceedings of the International Conference on Machine Learning}, | |
| Year = {2012}, | |
| Address = {Edinburgh, Scotland}, | |
| Month = {June}, | |
| Pages = {711--718}, | |
| Publisher = {Omnipress} | |
| } | |
| @InProceedings{elhamifar11cvpr, | |
| Title = {Robust classification using structured sparse representation}, | |
| Author = {Elhamifar, E. and Vidal, R.}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2011}, | |
| Pages = {1873-1879}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.03.07} | |
| } | |
| @InProceedings{Farenzena2010, | |
| Title = {Person re-identification by symmetry-driven accumulation of local features}, | |
| Author = {Farenzena, M. and Bazzani, L. and Perina, A. and Murino, V. and Cristani, M.}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2010}, | |
| Pages = {2360-2367}, | |
| Doi = {10.1109/CVPR.2010.5539926}, | |
| ISSN = {1063-6919}, | |
| Keywords = {entropy;feature extraction;image colour analysis;appearance-based method;asymmetry perceptual principles;color spatial arrangement;feature extraction;high entropy;local features;overall chromatic content;person reidentification;recurrent local motifs;symmetry perceptual principles;symmetry-driven accumulation;Benchmark testing;Biological system modeling;Data mining;Entropy;Feature extraction;Humans;Lighting;Performance evaluation;Robustness;Spatial resolution} | |
| } | |
| @InProceedings{farhadiEHF2009cvpr, | |
| Title = {Describing objects by their attributes}, | |
| Author = {Farhadi, A. and Endres, I. and Hoiem, D. and Forsyth, D.}, | |
| Booktitle = {CVPR}, | |
| Year = {2009}, | |
| Month = {June}, | |
| Pages = {1778--1785}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{farquhar2006nips, | |
| Title = {Two view learning: {SVM-2K}, Theory and Practice}, | |
| Author = {Farquhar, J.D.R. and Hardoon, D. R. and Meng, H. and Shawe-Taylor, J. and Szedmak, S.}, | |
| Booktitle = {NIPS}, | |
| Year = {2006}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.07.17} | |
| } | |
| @InProceedings{fernandoHST13iccv, | |
| Title = {Unsupervised Visual Domain Adaptation Using Subspace Alignment}, | |
| Author = {B. Fernando and A. Habrard and M. Sebban and T. Tuytelaars}, | |
| Booktitle = {IEEE ICCV}, | |
| Year = {2013}, | |
| Pages = {2960-2967} | |
| } | |
| @InProceedings{ferrariZ2007nips, | |
| Title = {Learning Visual Attributes}, | |
| Author = {Ferrari, V. and Zisserman, A.}, | |
| Booktitle = {NIPS}, | |
| Year = {2007}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{feyereislKSH2014nips, | |
| Title = {Object Localization based on Structural {SVM} using Privileged Information}, | |
| Author = {Feyereisl, J. and Kwak, S. and Son, J. and Han, B.}, | |
| Booktitle = {NIPS}, | |
| Year = {2014}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{freeman1996computer, | |
| Title = {Computer vision for computer games}, | |
| Author = {Freeman, William T and Tanaka, Ken-ichi and Ohta, Jun and Kyuma, Kazuo}, | |
| Booktitle = {Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on}, | |
| Year = {1996}, | |
| Organization = {IEEE}, | |
| Pages = {100--105} | |
| } | |
| @InProceedings{frome2013devise, | |
| Title = {Devise: A deep visual-semantic embedding model}, | |
| Author = {Frome, Andrea and Corrado, Greg S and Shlens, Jon and Bengio, Samy and Dean, Jeff and Mikolov, Tomas and others}, | |
| Booktitle = {Advances in neural information processing systems}, | |
| Year = {2013}, | |
| Pages = {2121--2129} | |
| } | |
| @InProceedings{fromeSM07nips, | |
| Title = {Image Retrieval and Classification Using Local Distance Functions}, | |
| Author = {Frome, A. and Singer, Y. and Malik, J.}, | |
| Booktitle = {NIPS}, | |
| Year = {2007}, | |
| Pages = {417--424}, | |
| Volume = {19}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.11.13} | |
| } | |
| @InProceedings{Fusing2013Cui, | |
| Title = {Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild}, | |
| Author = {Zhen Cui and Wen Li and Dong Xu and Shiguang Shan and Xilin Chen}, | |
| Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on}, | |
| Year = {2013}, | |
| Month = {June}, | |
| Pages = {3554-3561}, | |
| Doi = {10.1109/CVPR.2013.456}, | |
| ISSN = {1063-6919}, | |
| Keywords = {face recognition;learning (artificial intelligence);principal component analysis;PMML;SFRD;STFRD;WPCA;YTF;YouTube faces;appearance variations;distance metric learning method;face images;face recognition;face verification;feature dimension reduction;low-quality images;nonnegative sparse codes;pairwise-constrained multiple metric learning;position-free patches;principal component analysis;real-world datasets LFW;restricted protocol;robust face region descriptor fusion;spatial blocks;spatial face region descriptor;spatial-temporal face region descriptor;Encoding;Face;Face recognition;Feature extraction;Measurement;Principal component analysis;Visualization} | |
| } | |
| @InProceedings{FXRQ_iccv13, | |
| Title = {Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias}, | |
| Author = {Chen Fang and Ye Xu and Daniel N. Rockmore}, | |
| Booktitle = {International Conference on Computer Vision}, | |
| Year = {2013} | |
| } | |
| @InProceedings{gehlerN2009iccv, | |
| Title = {On feature combination for multiclass object classification}, | |
| Author = {Gehler, P. and Nowozin, S.}, | |
| Booktitle = {ICCV}, | |
| Year = {2009}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{gheissariSTR06cvpr, | |
| Title = {Person reidentification using spatiotemporal appearance}, | |
| Author = {Gheissari, N. and Sebastian, T. B. and Tu, P. H. and Rittscher, J. and Hartley, R.}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2006}, | |
| Pages = {1528--1535}, | |
| Volume = {2}, | |
| Abstract = {In many surveillance applications it is desirable to determine if a given individual has been previously observed over a network of cameras. This is the person reidentification problem. This paper focuses on reidentification algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. Person reidentification approaches have two aspects: (i) establish correspondence between parts, and (ii) generate signatures that are invariant to variations in illumination, pose, and the dynamic appearance of clothing. A novel spatiotemporal segmentation algorithm is employed to generate salient edgels that are robust to changes in appearance of clothing. The invariant signatures are generated by combining normalized color and salient edgel histograms. Two approaches are proposed to generate correspondences: (i) a model based approach that fits an articulated model to each individual to establish a correspondence map, and (ii) an interest point operator approach that nominates a large number of potential correspondences which are evaluated using a region growing scheme. Finally, the approaches are evaluated on a 44 person database across 3 disparate views.}, | |
| File = {gheissariSTR06cvpr.pdf:sebastian\\gheissariSTR06cvpr.pdf:PDF;sebastian\\gheissariSTR06cvpr.pdf:sebastian\\gheissariSTR06cvpr.pdf:PDF}, | |
| ISSN = {1063-6919}, | |
| Owner = {doretto}, | |
| Timestamp = {2006.11.27} | |
| } | |
| @InProceedings{ghifary2015domain, | |
| Title = {Domain generalization for object recognition with multi-task autoencoders}, | |
| Author = {Ghifary, Muhammad and Bastiaan Kleijn, W and Zhang, Mengjie and Balduzzi, David}, | |
| Booktitle = {Proceedings of the IEEE International Conference on Computer Vision}, | |
| Year = {2015}, | |
| Pages = {2551--2559} | |
| } | |
| @InProceedings{ghifary2016deep, | |
| Title = {Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation}, | |
| Author = {Ghifary, Muhammad and Kleijn, W Bastiaan and Zhang, Mengjie and Balduzzi, David and Li, Wen}, | |
| Booktitle = {European Conference on Computer Vision}, | |
| Year = {2016}, | |
| Organization = {Springer}, | |
| Pages = {597--613} | |
| } | |
| @InProceedings{girshick2014rich, | |
| Title = {Rich feature hierarchies for accurate object detection and semantic segmentation}, | |
| Author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra}, | |
| Booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| Year = {2014}, | |
| Pages = {580--587} | |
| } | |
| @InProceedings{gong2012geodesic, | |
| Title = {Geodesic flow kernel for unsupervised domain adaptation}, | |
| Author = {Gong, Boqing and Shi, Yuan and Sha, Fei and Grauman, Kristen}, | |
| Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on}, | |
| Year = {2012}, | |
| Organization = {IEEE}, | |
| Pages = {2066--2073} | |
| } | |
| @InProceedings{gongGS13icml, | |
| Title = {Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation}, | |
| Author = {Gong, B. and Grauman, K. and Sha, F.}, | |
| Booktitle = {ICML}, | |
| Year = {2013} | |
| } | |
| @InProceedings{gongGS13nips, | |
| Title = {Reshaping Visual Datasets for Domain Adaptation}, | |
| Author = {Gong, B. and Grauman, K. and Sha, F.}, | |
| Booktitle = {NIPS}, | |
| Year = {2013} | |
| } | |
| @InProceedings{gopalanLC11iccv, | |
| Title = {Domain adaptation for object recognition: An unsupervised approach}, | |
| Author = {Gopalan, R. and Li, R. and Chellappa, R.}, | |
| Booktitle = {IEEE ICCV}, | |
| Year = {2011}, | |
| Pages = {999-1006} | |
| } | |
| @InProceedings{grettonBRSS06nips, | |
| Title = {A Kernel Method for the Two-Sample-Problem}, | |
| Author = {Gretton, A. and Borgwardt, K. M. and Rasch, M. and Sch\"olkopf, B. and Smola, A. J.}, | |
| Booktitle = {NIPS}, | |
| Year = {2006} | |
| } | |
| @InProceedings{GuoX12, | |
| Title = {Cross Language Text Classification via Subspace Co-regularized Multi-view | |
| Learning}, | |
| Author = {Yuhong Guo and | |
| Min Xiao}, | |
| Booktitle = {Proceedings of the 29th International Conference on Machine Learning, | |
| {ICML} 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012}, | |
| Year = {2012} | |
| } | |
| @InProceedings{hadi18, | |
| Title = {Facial Attributes Guided Deep Sketch-to-Photo Synthesis}, | |
| Author = {Kazemi, Hadi and Iranmanesh, Mehdi and Dabouei, Ali and Nasrabadi, Nasser M.}, | |
| Booktitle = {Applications of Computer Vision (WACV), 2018 IEEE Workshop on}, | |
| Year = {2018}, | |
| Organization = {IEEE} | |
| } | |
| @InProceedings{hadsell2006dimensionality, | |
| Title = {Dimensionality reduction by learning an invariant mapping}, | |
| Author = {Hadsell, Raia and Chopra, Sumit and LeCun, Yann}, | |
| Booktitle = {Computer vision and pattern recognition, 2006 IEEE computer society conference on}, | |
| Year = {2006}, | |
| Organization = {IEEE}, | |
| Pages = {1735--1742}, | |
| Volume = {2} | |
| } | |
| @InProceedings{he2016deep, | |
| Title = {Deep residual learning for image recognition}, | |
| Author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, | |
| Booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
| Year = {2016}, | |
| Pages = {770--778} | |
| } | |
| @InProceedings{hoffer2015deep, | |
| Title = {Deep metric learning using triplet network}, | |
| Author = {Hoffer, Elad and Ailon, Nir}, | |
| Booktitle = {International Workshop on Similarity-Based Pattern Recognition}, | |
| Year = {2015}, | |
| Organization = {Springer}, | |
| Pages = {84--92} | |
| } | |
| @InProceedings{Hoffman_ICLR2013, | |
| Title = {Efficient Learning of Domain-invariant Image Representations}, | |
| Author = {Judy Hoffman and Erik Rodner and Jeff Donahue and Kate Saenko and Trevor Darrell}, | |
| Booktitle = {International Conference on Learning Representations}, | |
| Year = {2013} | |
| } | |
| @InProceedings{hoffman2016learning, | |
| Title = {Learning with side information through modality hallucination}, | |
| Author = {Hoffman, Judy and Gupta, Saurabh and Darrell, Trevor}, | |
| Booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | |
| Year = {2016}, | |
| Pages = {826--834} | |
| } | |
| @InProceedings{hoffmanKDS12eccv, | |
| Title = {Discovering Latent Domains for Multisource Domain Adaptation}, | |
| Author = {Hoffman, J. and Kulis, B. and Darrell, T. and Saenko, K.}, | |
| Booktitle = {ECCV}, | |
| Year = {2012}, | |
| Pages = {702-715} | |
| } | |
| @InProceedings{hoiJL2007icml, | |
| Title = {Learning Nonparametric Kernel Matrices from Pairwise Constraints}, | |
| Author = {Hoi, S. C. H. and Jin, R. and Lyu, M. R.}, | |
| Booktitle = {ICML}, | |
| Year = {2007}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{Hong2012Robust, | |
| Title = {Robust visual domain adaptation with low-rank reconstruction}, | |
| Author = {I-Hong Jhuo and Dong Liu and Lee, D.T. and Shih-Fu Chang}, | |
| Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on}, | |
| Year = {2012}, | |
| Month = {June}, | |
| Pages = {2168-2175}, | |
| Doi = {10.1109/CVPR.2012.6247924}, | |
| ISSN = {1063-6919} | |
| } | |
| @InProceedings{Huang_2017_CVPR, | |
| Title = {Densely Connected Convolutional Networks}, | |
| Author = {Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q.}, | |
| Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| Year = {2017}, | |
| Month = {July} | |
| } | |
| @InProceedings{huang2012learning, | |
| Title = {Learning to align from scratch}, | |
| Author = {Huang, Gary and Mattar, Marwan and Lee, Honglak and Learned-Miller, Erik G}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
| Year = {2012}, | |
| Pages = {764--772} | |
| } | |
| @InProceedings{huangSGBS06nips, | |
| Title = {Correcting Sample Selection Bias by Unlabeled Data}, | |
| Author = {Huang, J. and Smola, A. J. and Gretton, A. and Borgwardt, K. M. and Sch\"olkopf, B.}, | |
| Booktitle = {NIPS}, | |
| Year = {2006} | |
| } | |
| @InProceedings{huynhMD12accvw, | |
| Title = {An Efficient {LBP}-Based Descriptor for Facial Depth Images Applied to Gender Recognition Using {RGB-D} Face Data}, | |
| Author = {Huynh, T. and Min, R.. and Dugelay, J.}, | |
| Booktitle = {ACCV Workshops}, | |
| Year = {2012}, | |
| Pages = {133--145} | |
| } | |
| @InProceedings{Information2007Davis, | |
| Title = {Information-theoretic Metric Learning}, | |
| Author = {Davis, Jason V. and Kulis, Brian and Jain, Prateek and Sra, Suvrit and Dhillon, Inderjit S.}, | |
| Booktitle = {Proceedings of the 24th International Conference on Machine Learning}, | |
| Year = {2007}, | |
| Address = {New York, NY, USA}, | |
| Pages = {209--216}, | |
| Publisher = {ACM}, | |
| Series = {ICML '07}, | |
| Acmid = {1273523}, | |
| Doi = {10.1145/1273496.1273523}, | |
| ISBN = {978-1-59593-793-3}, | |
| Location = {Corvalis, Oregon}, | |
| Numpages = {8}, | |
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| Booktitle = {Biometrics (ICB), 2018 International Conference on}, | |
| Year = {2018}, | |
| Organization = {IEEE} | |
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| Year = {2009}, | |
| Month = {Sept}, | |
| Pages = {498-505}, | |
| Doi = {10.1109/ICCV.2009.5459197}, | |
| ISSN = {1550-5499}, | |
| Keywords = {face recognition;image classification;learning (artificial intelligence);LDML;MkNN;data set;evaluation protocol;face identification;logistic discriminant approach;metric learning approach;nearest neighbour approach;state-of-the-art method;Active contours;Biomedical computing;Computational complexity;Computer science;Graph theory;Image segmentation;Kernel;Level set;Optimization methods;Pixel} | |
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| Booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | |
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| Keywords = {distance measurement;learning (artificial intelligence);Mahalanobis metric learning methods;complex optimization problems;computationally expensive iterations;distance metric learning;equivalence constraints;large scale metric learning;spatially disjoint cameras;statistical inference perspective;Benchmark testing;Databases;Measurement;Optimization;Scalability;Support vector machines;Training} | |
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| Booktitle = {ECCV}, | |
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| Title = {POP: Person Re-identification Post-rank Optimisation}, | |
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| Keywords = {human computer interaction;information retrieval;manifold;person re-identification;ranking;visual surveillance}, | |
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| Booktitle = {CVPR}, | |
| Year = {2013}, | |
| Month = {June}, | |
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| Booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition}, | |
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| Title = {Local Descriptors Encoded by Fisher Vectors for Person Re-identification}, | |
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| Booktitle = {Proceedings of the 12th International Conference on Computer Vision - Volume Part I}, | |
| Year = {2012}, | |
| Address = {Berlin, Heidelberg}, | |
| Pages = {413--422}, | |
| Publisher = {Springer-Verlag}, | |
| Series = {ECCV'12}, | |
| Acmid = {2403356}, | |
| Doi = {10.1007/978-3-642-33863-2_41}, | |
| ISBN = {978-3-642-33862-5}, | |
| Location = {Florence, Italy}, | |
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| Url = {http://dx.doi.org/10.1007/978-3-642-33863-2_41} | |
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| Title = {BiCov: a novel image representation for person re-identification and face verification}, | |
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| Booktitle = {BMVC}, | |
| Year = {2012}, | |
| Pages = {57.1--57.11}, | |
| Publisher = {BMVA Press}, | |
| Doi = {http://dx.doi.org/10.5244/C.26.57}, | |
| Editors = {Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian}, | |
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| Title = {Sparse representation for face recognition based on discriminative low-rank dictionary learning}, | |
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| Booktitle = {IEEE CVPR}, | |
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| Booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | |
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| Booktitle = {IEEE CVPR}, | |
| Year = {2008}, | |
| Month = {June}, | |
| Pages = {1-8}, | |
| Owner = {doretto}, | |
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| Title = {Metric learning to rank}, | |
| Author = {McFee, Brian and Lanckriet, Gert R}, | |
| Booktitle = {Proceedings of the 27th International Conference on Machine Learning (ICML-10)}, | |
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| @InProceedings{mejjati2018unsupervised, | |
| Title = {Unsupervised Attention-guided Image-to-Image Translation}, | |
| Author = {Mejjati, Youssef Alami and Richardt, Christian and Tompkin, James and Cosker, Darren and Kim, Kwang In}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
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| Title = {Robust sparse coding for face recognition}, | |
| Author = {Meng, Y. and Zhang, D. and Jian, Y. and Zhang, D.}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2011}, | |
| Pages = {625-632}, | |
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| Booktitle = {ICCV Workshop}, | |
| Year = {2011}, | |
| Month = {Nov}, | |
| Pages = {593-600}, | |
| Doi = {10.1109/ICCVW.2011.6130297}, | |
| Keywords = {calibration;cameras;feature extraction;image reconstruction;image texture;natural scenes;optimisation;solid modelling;town and country planning;3D models;camera geometry reconstruction;consistent camera calibration;global features;high-dimensional optimization technique;holistic 3D reconstruction;large-scale buildings;multiple large-baseline uncalibrated images;scene geometry;semiglobal features;transform invariant low-rank textures;urban scenes;urban structures;Buildings;Cameras;Encoding;Feature extraction;Image coding;Image edge detection;Three dimensional displays} | |
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| Title = {Naturalistic pain synthesis for virtual patients}, | |
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| Booktitle = {Intelligent Virtual Agents}, | |
| Year = {2014}, | |
| Organization = {Springer}, | |
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| Booktitle = {Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, | |
| Year = {2017}, | |
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| Title = {Unified Deep Supervised Domain Adaptation and Generalization}, | |
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| Booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, | |
| Year = {2017}, | |
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| @InProceedings{motiian2013pairwise, | |
| Title = {Pairwise kernels for human interaction recognition}, | |
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| Booktitle = {International Symposium on Visual Computing}, | |
| Year = {2013}, | |
| Organization = {Springer}, | |
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| Title = {Information Bottleneck Domain Adaptation with Privileged Information for Visual Recognition}, | |
| Author = {Motiian, Saeid and Doretto, Gianfranco}, | |
| Booktitle = {European Conference on Computer Vision}, | |
| Year = {2016}, | |
| Organization = {Springer}, | |
| Pages = {630--647} | |
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| @InProceedings{motiian2016information, | |
| Title = {Information bottleneck learning using privileged information for visual recognition}, | |
| Author = {Motiian, Saeid and Piccirilli, Marco and Adjeroh, Donald A and Doretto, Gianfranco}, | |
| Booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | |
| Year = {2016}, | |
| Pages = {1496--1505} | |
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| Title = {Few-Shot Adversarial Domain Adaptation}, | |
| Author = {Motiian, Saeid and Jones, Quinn and Iranmanesh, Seyed and Doretto, Gianfranco}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
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| Title = {Domain Generalization via Invariant Feature Representation.}, | |
| Author = {Muandet, Krikamol and Balduzzi, David and Sch{\"o}lkopf, Bernhard}, | |
| Booktitle = {ICML (1)}, | |
| Year = {2013}, | |
| Pages = {10--18} | |
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| @InProceedings{netzer2011reading, | |
| Title = {Reading digits in natural images with unsupervised feature learning}, | |
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| Booktitle = {NIPS workshop on deep learning and unsupervised feature learning}, | |
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| Title = {Multimodal Deep Learning}, | |
| Author = {Ngiam, J. and Khosla, A. and Kim, M. and Nam, J. and Lee, H. and Ng, A. Y.}, | |
| Booktitle = {ICML}, | |
| Year = {2011}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
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| @InProceedings{niu2015multi, | |
| Title = {Multi-view domain generalization for visual recognition}, | |
| Author = {Niu, Li and Li, Wen and Xu, Dong}, | |
| Booktitle = {Proceedings of the IEEE International Conference on Computer Vision}, | |
| Year = {2015}, | |
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| Title = {Jointly modeling embedding and translation to bridge video and language}, | |
| Author = {Pan, Yingwei and Mei, Tao and Yao, Ting and Li, Houqiang and Rui, Yong}, | |
| Booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, | |
| Year = {2016}, | |
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| @InProceedings{patel2013latent, | |
| Title = {Latent Space Sparse Subspace Clustering}, | |
| Author = {Patel, Vishal M and Van Nguyen, Hien and Vidal, Ren{\'e}}, | |
| Booktitle = {ICCV}, | |
| Year = {2013} | |
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| @InProceedings{PCCA2012Mignon, | |
| Title = {PCCA: A new approach for distance learning from sparse pairwise constraints}, | |
| Author = {Mignon, A. and Jurie, F.}, | |
| Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on}, | |
| Year = {2012}, | |
| Month = {June}, | |
| Pages = {2666-2672}, | |
| Doi = {10.1109/CVPR.2012.6247987}, | |
| ISSN = {1063-6919}, | |
| Keywords = {computer vision;face recognition;generalisation (artificial intelligence);learning (artificial intelligence);distance learning;face verification;generalization property;high dimensional data;learning distance metrics;pairwise constrained component analysis;person reidentification;sparse pairwise constraint;sparse pairwise dissimilarity constraints;sparse pairwise similarity constraints;vision task;Face;Histograms;Kernel;Measurement;Training;Training data;Vectors} | |
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| @InProceedings{pechyonyV2010nips, | |
| Title = {On the Theory of Learning with Privileged Information}, | |
| Author = {Pechyony, D. and Vapnik, V.}, | |
| Booktitle = {NIPS}, | |
| Year = {2010}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.16} | |
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| @InProceedings{pinto2016curious, | |
| Title = {The curious robot: Learning visual representations via physical interactions}, | |
| Author = {Pinto, Lerrel and Gandhi, Dhiraj and Han, Yuanfeng and Park, Yong-Lae and Gupta, Abhinav}, | |
| Booktitle = {European Conference on Computer Vision}, | |
| Year = {2016}, | |
| Organization = {Springer}, | |
| Pages = {3--18} | |
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| Title = {Learning Discriminative Appearance-Based Models Using Partial Least Squares}, | |
| Author = {Schwartz, W.R. and Davis, L.S.}, | |
| Booktitle = {SIBGRAPI}, | |
| Year = {2009}, | |
| Month = {Oct}, | |
| Pages = {322-329}, | |
| Doi = {10.1109/SIBGRAPI.2009.42}, | |
| ISSN = {1550-1834}, | |
| Keywords = {image colour analysis;learning (artificial intelligence);least squares approximations;object recognition;appearance based person recognition;feature descriptors;learning discriminative appearance based models;machine learning techniques;partial least squares;Computer graphics;Data mining;Filtering;Image analysis;Image processing;Informatics;Least squares methods;Signal analysis;Signal processing;Wavelet analysis;Appearance-Based Models;Co-occurrence matrices;HOG;PLS;Partial Least Squares} | |
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| @InProceedings{prosser2010person, | |
| Title = {Person Re-Identification by Support Vector Ranking.}, | |
| Author = {Prosser, Bryan and Zheng, Wei-Shi and Gong, Shaogang and Xiang, Tao and Mary, Q}, | |
| Booktitle = {BMVC}, | |
| Year = {2010}, | |
| Number = {3}, | |
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| @InProceedings{Quadruplet2013law, | |
| Title = {Quadruplet-Wise Image Similarity Learning}, | |
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| Booktitle = {Computer Vision (ICCV), 2013 IEEE International Conference on}, | |
| Year = {2013}, | |
| Month = {Dec}, | |
| Pages = {249-256}, | |
| Doi = {10.1109/ICCV.2013.38}, | |
| ISSN = {1550-5499}, | |
| Keywords = {convex programming;image processing;learning (artificial intelligence);class ranking;class taxonomy;convex optimization scheme;inequality constraints;learning framework;quadruplet-wise constraints;quadruplet-wise image similarity learning;semantic label;webpage screenshots;Accuracy;Context;Face;Measurement;Optimization;Training;Vectors;machine learning;metric learning} | |
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| @InProceedings{ranksvm_2010, | |
| Title = {Person Re-Identification by Support Vector Ranking}, | |
| Author = {Prosser, Bryan and Zheng, Wei-Shi and Gong, Shaogang and Xiang, Tao}, | |
| Booktitle = {Proceedings of the British Machine Vision Conference}, | |
| Year = {2010}, | |
| Note = {doi:10.5244/C.24.21}, | |
| Pages = {21.1--21.11}, | |
| Publisher = {BMVA Press}, | |
| Editors = {Labrosse, Fr\'ed\'eric and Zwiggelaar, Reyer and Liu, Yonghuai and Tiddeman, Bernie}, | |
| ISBN = {1-901725-40-5} | |
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| @InProceedings{reed2016generative, | |
| Title = {Generative adversarial text to image synthesis}, | |
| Author = {Reed, Scott and Akata, Zeynep and Yan, Xinchen and Logeswaran, Lajanugen and Schiele, Bernt and Lee, Honglak}, | |
| Booktitle = {Proceedings of the 33rd International Conference on International Conference on Machine Learning-Volume 48}, | |
| Year = {2016}, | |
| Organization = {JMLR. org}, | |
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| Title = {Learning what and where to draw}, | |
| Author = {Reed, Scott E and Akata, Zeynep and Mohan, Santosh and Tenka, Samuel and Schiele, Bernt and Lee, Honglak}, | |
| Booktitle = {Advances in Neural Information Processing Systems}, | |
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| Title = {Joint Image-Text Representation by Gaussian Visual-Semantic Embedding}, | |
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| Booktitle = {Proceedings of the 2016 ACM on Multimedia Conference}, | |
| Year = {2016}, | |
| Organization = {ACM}, | |
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| @InProceedings{Risheng2012Fixed, | |
| Title = {Fixed-rank representation for unsupervised visual learning}, | |
| Author = {Risheng Liu and Zhouchen Lin and De la Torre, F. and Zhixun Su}, | |
| Booktitle = {CVPR}, | |
| Year = {2012}, | |
| Month = {June}, | |
| Pages = {598-605}, | |
| Doi = {10.1109/CVPR.2012.6247726}, | |
| ISSN = {1063-6919}, | |
| Keywords = {computer vision;feature extraction;matrix decomposition;minimisation;pattern clustering;sparse matrices;unsupervised learning;FRR framework;clustering performance;computationally expensive;computer vision;degenerate solutions;fixed-rank representation;insufficient data sampling;insufficient observations;matrix factorization;multiple subspaces;nontrivial byproduct;numerical solver;pattern recognition;rank minimization;sparse regularizer;sparsity;state-of-the-art techniques;subspace clustering;synthetic data;theoretical analysis;true subspace memberships;unsupervised feature extraction;unsupervised learning techniques;unsupervised visual learning;Clustering algorithms;Feature extraction;Minimization;Noise;Principal component analysis;Vectors;Visualization} | |
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| @InProceedings{saenkoKFD2010eccv, | |
| Title = {Adapting Visual Category Models to New Domains}, | |
| Author = {Saenko, K. and Kulis, B. and Fritz, M. and Darrell, T.}, | |
| Booktitle = {ECCV}, | |
| Year = {2010}, | |
| Pages = {213--226}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
| } | |
| @InProceedings{sarafianos2016predicting, | |
| Title = {Predicting privileged information for height estimation}, | |
| Author = {Sarafianos, Nikolaos and Nikou, Christophoros and Kakadiaris, Ioannis A}, | |
| Booktitle = {Pattern Recognition (ICPR), 2016 23rd International Conference on}, | |
| Year = {2016}, | |
| Organization = {IEEE}, | |
| Pages = {3115--3120} | |
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| @InProceedings{Schwartz2009, | |
| Title = {Learning Discriminative Appearance-Based Models Using Partial Least Squares}, | |
| Author = {Schwartz, William Robson and Davis, Larry S.}, | |
| Booktitle = {Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing}, | |
| Year = {2009}, | |
| Address = {Washington, DC, USA}, | |
| Pages = {322--329}, | |
| Publisher = {IEEE Computer Society}, | |
| Series = {SIBGRAPI '09}, | |
| Acmid = {1730330}, | |
| Doi = {10.1109/SIBGRAPI.2009.42}, | |
| ISBN = {978-0-7695-3813-6}, | |
| Keywords = {Appearance-Based Models, Partial Least Squares, PLS, HOG, Co-occurrence matrices}, | |
| Numpages = {8}, | |
| Url = {http://dx.doi.org/10.1109/SIBGRAPI.2009.42} | |
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| @InProceedings{Shahroudy_2016_CVPR, | |
| Title = {NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis}, | |
| Author = {Shahroudy, Amir and Liu, Jun and Ng, Tian-Tsong and Wang, Gang}, | |
| Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| Year = {2016}, | |
| Month = {June} | |
| } | |
| @InProceedings{sharmanskaQL2013iccv, | |
| author = {Sharmanska, V. and Quadrianto, N. and Lampert, C.H.}, | |
| booktitle = {IEEE ICCV}, | |
| title = {Learning to Rank Using Privileged Information}, | |
| year = {2013}, | |
| pages = {825--832}, | |
| groups = {doretto:6}, | |
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| @InProceedings{shen2009positive, | |
| Title = {Positive semidefinite metric learning with boosting}, | |
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| Booktitle = {Advances in Neural Information Processing Systems}, | |
| Year = {2009}, | |
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| @InProceedings{shen2012unified, | |
| Title = {A unified approach to salient object detection via low rank matrix recovery}, | |
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| Booktitle = {CVPR}, | |
| Year = {2012}, | |
| Month = {June}, | |
| Pages = {853-860}, | |
| Doi = {10.1109/CVPR.2012.6247758}, | |
| ISSN = {1063-6919}, | |
| Keywords = {feature extraction;matrix algebra;object detection;higher-level guidance;higher-level knowledge;low rank matrix recovery;low-level features;salient object detection;sparse noises;task-dependent saliency applications;task-independent image saliency;top-down priors;unified approach;Feature extraction;Image color analysis;Image segmentation;Matrix decomposition;Noise;Sparse matrices;Vectors} | |
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| Booktitle = {ICML}, | |
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| Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| Year = {2017}, | |
| Month = {July} | |
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| Title = {Approaches of artificial intelligence in biomedical image processing: A leading tool between computer vision \& biological vision}, | |
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| Booktitle = {Advances in Computing, Communication, \& Automation (ICACCA)(Spring), International Conference on}, | |
| Year = {2016}, | |
| Organization = {IEEE}, | |
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| Booktitle = {Multimedia and Expo (ICME), 2014 IEEE International Conference on}, | |
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| Year = {2015}, | |
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| Year = {2017}, | |
| Organization = {IEEE}, | |
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| Booktitle = {ECCV}, | |
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| Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on}, | |
| Year = {2013}, | |
| Month = {June}, | |
| Pages = {3586-3593}, | |
| Doi = {10.1109/CVPR.2013.460}, | |
| ISSN = {1063-6919}, | |
| Keywords = {feature extraction;image matching;learning (artificial intelligence);ETHZ dataset;VIPeR dataset;adjacency constrained patch matching;camera views;discriminative feature learning;distinctive feature extraction;drastic viewpoint change;human eyes;image pairs;image similarities;person re-identification;training data;training procedure;unsupervised salience learning;valuable salient information;Cameras;Feature extraction;Histograms;Image color analysis;Support vector machines;Training;Vectors;Salience matching;person re-identification;recognition} | |
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| Booktitle = {ICPR}, | |
| Year = {2014}, | |
| Month = {Aug}, | |
| Pages = {1389-1394}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
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| Booktitle = {CVPR}, | |
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| Pages = {4969--4977}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
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| Booktitle = {Document Analysis and Recognition (ICDAR), 2013 12th International Conference on}, | |
| Year = {2013}, | |
| Month = {Aug}, | |
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| Doi = {10.1109/ICDAR.2013.86}, | |
| ISSN = {1520-5363}, | |
| Keywords = {image texture;minimisation;optical character recognition;Chinese character;English character;TILT algorithm;affine deformation;binarization;character recognition;digit character;front view standard character images;gray level inversion;optical character rectification;perspective deformation;rank minimization technique;recognition algorithms;text rectification methods;transform invariant low-rank textures;Character recognition;Discrete cosine transforms;Linear programming;Optical character recognition software;Robustness;Standards} | |
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| Booktitle = {ECCV}, | |
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| Booktitle = {IEEE FG}, | |
| Year = {2013}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
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| Booktitle = {ECCV}, | |
| Year = {2010}, | |
| Pages = {448--461}, | |
| ISBN = {3-642-15566-9, 978-3-642-15566-6}, | |
| Location = {Heraklion, Crete, Greece}, | |
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| Timestamp = {2014.06.01} | |
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| Title = {{Kullback-Leibler} Divergence for Nonnegative Matrix Factorization}, | |
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| Booktitle = {ICANN}, | |
| Year = {2011}, | |
| Pages = {250--257}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.22} | |
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| Title = {Semi-Supervised Domain Adaptation With Subspace Learning for Visual Recognition}, | |
| Author = {Yao, Ting and Pan, Yingwei and Ngo, Chong-Wah and Li, Houqiang and Mei, Tao}, | |
| Booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| Year = {2015}, | |
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| Booktitle = {KDD}, | |
| Year = {2012}, | |
| Pages = {1469--1477}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.11.14} | |
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| Title = {Camera calibration with lens distortion from low-rank textures}, | |
| Author = {Zhengdong Zhang and Matsushita, Y. and Yi Ma}, | |
| Booktitle = {CVPR}, | |
| Year = {2011}, | |
| Month = {June}, | |
| Pages = {2321-2328}, | |
| Doi = {10.1109/CVPR.2011.5995548}, | |
| ISSN = {1063-6919}, | |
| Keywords = {calibration;cameras;image texture;minimisation;principal component analysis;camera calibration problem;camera parameters;high dimensional convex optimization tool;intrinsic parameter calibration;large lens distortion;low rank texture;matrix rank minimization;principal component pursuit;sparse signal recovery;Calibration;Cameras;Feature extraction;Lenses;Lighting;Manganese;Optimization} | |
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| @InProceedings{zhang2011image, | |
| Title = {Image classification by non-negative sparse coding, low-rank and sparse decomposition}, | |
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| Booktitle = {CVPR}, | |
| Year = {2011}, | |
| Month = {June}, | |
| Pages = {1673-1680}, | |
| Doi = {10.1109/CVPR.2011.5995484}, | |
| ISSN = {1063-6919}, | |
| Keywords = {feature extraction;image classification;image coding;image matching;image representation;matrix decomposition;sparse matrices;support vector machines;Sc+ SPM image representation;image classification;image representation;linear SVM classifier;locality constrained image linear coding;max pooling;nonnegative sparse coding;sparse error matrix;sparse matrix decomposition technique;sparse matrix recovery technique;spatial pyramid matching;spatial pyramid matching method;Encoding;Feature extraction;Image coding;Matrix decomposition;Noise;Sparse matrices;Training} | |
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| Title = {Low-Rank Sparse Learning for Robust Visual Tracking}, | |
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| Booktitle = {Proc. ECCV}, | |
| Year = {2012}, | |
| Pages = {470-484}, | |
| Series = {Lecture Notes in Computer Science}, | |
| Volume = {7577}, | |
| Owner = {doretto}, | |
| Timestamp = {2014.03.07} | |
| } | |
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| Author = {Zhang, Yangmuzi and Jiang, Zhuolin and Davis, Larry S}, | |
| Booktitle = {IEEE CVPR}, | |
| Year = {2013}, | |
| Pages = {676--683} | |
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| Title = {Can Visual Recognition Benefit from Auxiliary Information in Training?}, | |
| Author = {Zhang, Q. and Hua, G. and Liu, W. and Liu, Z. and Zhang, Z.}, | |
| Booktitle = {ACCV}, | |
| Year = {2014}, | |
| Pages = {65--80}, | |
| Owner = {doretto}, | |
| Timestamp = {2015.04.19} | |
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| Author = {Zhang, Q. and Hua, G.}, | |
| Booktitle = {ACM MM}, | |
| Year = {2015}, | |
| Pages = {561--570}, | |
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| year={2006}, | |
| publisher={American Association for the Advancement of Science} | |
| } | |
| @article{karras2020training, | |
| title={Training generative adversarial networks with limited data}, | |
| author={Karras, Tero and Aittala, Miika and Hellsten, Janne and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo}, | |
| journal={arXiv preprint arXiv:2006.06676}, | |
| year={2020} | |
| } | |
| @misc{wang2020covidnet, | |
| title={COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images}, | |
| author={Linda Wang, Zhong Qiu Lin and Alexander Wong}, | |
| year={2020}, | |
| eprint={2003.09871}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| @Comment{jabref-meta: databaseType:bibtex;} |
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