Peiyun Hu
I am a research scientist at Apple with Stephan Richter and Vladlen Koltun. Previously, I worked at Apple SPG and Argo AI. I did my PhD in Robotics at Robotics Institute, Carnegie Mellon University, where I was advised by Deva Ramanan. Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting Tarasha Khurana*, Peiyun Hu*, David Held, Deva Ramanan Computer Vision and Pattern Recognition (CVPR
COCO - Common Objects in Context
What is COCO? COCO is a new image recognition, segmentation, and captioning dataset. COCO has several features: Object segmentation Recognition in Context Multiple objects per image More than 300,000 images More than 2 Million instances 80 object categories 5 captions per image Keypoints on 100,000 people Tsung-Yi Lin Cornell Tech Genevieve Patterson MSR Matteo Ruggero Ronchi Caltech Yin Cui Corne
CRF as RNN Semantic Image Segmentation
CRF as RNN Semantic Image Segmentation Live Demo Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of the object. Currently we have trained this model to recognize 20 classes. The demo below allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you
SSD: Single Shot MultiBox Detector
We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box
Reading Text in the Wild with Convolutional Neural Networks
In this work we present an end-to-end system for text spotting -- localising and recognising text in natural scene images -- and text based image retrieval. This system is based on a region proposal mechanism for detection and deep convolutional neural networks for recognition. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast s
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines reg
Rodrigo Benenson github page
The Cityscapes Dataset M. Cordts, M. Omran, S. Ramos, T. Scharwächter, M. Enzweiler, R. Benenson, U. Franke, , B. Schiele Presented at CVPR workshop (unpublished) Chef's recommendation @inproceedings{Cordts2015Cvprw, title={The Cityscapes Dataset}, author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Scharw{\"a}chter, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe a
FDDB : Main
Welcome to the Face Detection Data Set and Benchmark (FDDB), a data set of face regions designed for studying the problem of unconstrained face detection. This data set contains the annotations for 5171 faces in a set of 2845 images taken from the Faces in the Wild data set. More details can be found in the technical report below. Explore the database: Sample annotations Frequently asked questions
1
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
GitHub - zhangliliang/RPN_BF: RPN+BF
GitHub - weiliu89/caffe at ssd
Alexandre Alahi - Research
chhshen - Overview
{{#tags}}- {{label}}
{{/tags}}