T4V @ CVPR 2022 - Schedule
Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer Is Large-scale Pre-training always Necessary for Vision Transformers? Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks MC-SSL: Towards Multi-Concept Self-Supervised Learning Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning VideoMAE: Maske
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
コンピュータビジョンã®æœ€æ–°è«–文調査 3D Human Pose Estimation ç·¨ | BLOG - DeNA Engineering
ã¯ã˜ã‚ã« ã“ã‚“ã«ã¡ã¯ã€AIシステム部ã§ã‚³ãƒ³ãƒ”ュータビジョンã®ç ”究開発をã—ã¦ã„ã‚‹åŠ è—¤ã§ã™ã€‚我々ã®ãƒãƒ¼ãƒ ã§ã¯ã€å¸¸ã«æœ€æ–°ã®ã‚³ãƒ³ãƒ”ュータビジョンã«é–¢ã™ã‚‹è«–文調査を行ã„ã€éƒ¨å†…ã§å…±æœ‰ãƒ»è°è«–ã—ã¦ã„ã¾ã™ã€‚å‰å›žã® 2D Human Pose Estimation ç·¨ ã«å¼•ã続ãã€ä»Šå›žã¯ 3D Human Pose Estimation ç·¨ã¨ã—ã¦åŠ 藤直樹 ( @nk35jk ) ãŒèª¿æŸ»ã‚’è¡Œã„ã¾ã—ãŸã€‚ 本記事ã§ã¯ 3D Human Pose Estimation ã«é–¢ã™ã‚‹ä»£è¡¨çš„ãªç ”究事例を紹介ã™ã‚‹ã¨ã¨ã‚‚ã«ã€ã‚³ãƒ³ãƒ”ュータビジョンã®ãƒˆãƒƒãƒ—カンファレンスã§ã‚ã‚‹ ICCV 2019 ã«æŽ¡éŒ²ã•ã‚ŒãŸè«–文をä¸å¿ƒã« 3D Human Pose Estimation ã®æœ€æ–°ã®ç ”究動å‘を紹介ã—ã¾ã™ã€‚ éŽåŽ»ã®ä»–タスク編ã«ã¤ã„ã¦ã¯ä»¥ä¸‹ã‚’ã”å‚ç…§ãã ã•ã„。 Human Recognition ç·¨ (2019/04/26) 3D Visio
コンピュータビジョンã®æœ€æ–°è«–文調査 2D Human Pose Estimation ç·¨ | BLOG - DeNA Engineering
ã¯ã˜ã‚ã« ã“ã‚“ã«ã¡ã¯ã€AIシステム部ã§ã‚³ãƒ³ãƒ”ュータビジョンã®ç ”究開発をã—ã¦ã„ã‚‹åŠ è—¤ã§ã™ã€‚我々ã®ãƒãƒ¼ãƒ ã§ã¯ã€å¸¸ã«æœ€æ–°ã®ã‚³ãƒ³ãƒ”ュータビジョンã«é–¢ã™ã‚‹è«–文調査を行ã„ã€éƒ¨å†…ã§å…±æœ‰ãƒ»è°è«–ã—ã¦ã„ã¾ã™ã€‚今回㯠2D Human Pose Estimation ç·¨ã¨ã—ã¦åŠ 藤直樹 ( @nk35jk ) ãŒèª¿æŸ»ã‚’è¡Œã„ã¾ã—ãŸã€‚ 本記事ã§ã¯ 2D Human Pose Estimation ã«é–¢ã™ã‚‹ä»£è¡¨çš„ãªç ”究事例を紹介ã™ã‚‹ã¨ã¨ã‚‚ã«ã€2019å¹´10月ã‹ã‚‰11月ã«ã‹ã‘ã¦é–‹å‚¬ã•ã‚ŒãŸã‚³ãƒ³ãƒ”ュータビジョンã®ãƒˆãƒƒãƒ—カンファレンスã§ã‚ã‚‹ ICCV 2019 ã«æŽ¡éŒ²ã•ã‚ŒãŸ 2D Human Pose Estimation ã®æœ€æ–°è«–文を紹介ã—ã¾ã™ã€‚ éŽåŽ»ã®ä»–タスク編ã«ã¤ã„ã¦ã¯ä»¥ä¸‹ã‚’ã”å‚ç…§ãã ã•ã„。 Human Recognition ç·¨ (2019/04/26) 3D Vision ç·¨ (2019/06/04) ã‚ーãƒã‚¤ãƒ³ãƒˆæ¤œ
Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images - MIT
♰ Universitat Politecnica de Catalunya ✦ Massachusetts Institute of Technology ✥ Qatar Computing Research Institute Abstract In this work we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retr
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this pa
CVPR 2020 Workshop
Third Workshop on Computer Vision for Fashion, Art and Design Creative domains render a big part of modern society, having a strong influence on the economy and cultural life. Much effort within creative domains, such as fashion, art and design, center around the creation, consumption, manipulation and analytics of visual content. In recent years, there has been an explosion of research in applyin
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StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval
IndoFashion
http://web.cs.ucla.edu/~yuanluxu/publications/fashion_grammar_cvpr18.pdf
GitHub - gcucurull/visual-compatibility: Context-Aware Visual Compatibility Prediction (https://arxiv.org/abs/1902.03646)
CVPR 2019 Open Access Repository
The surprising impact of mask-head architecture on novel class segmentation
[CVPR 2020 - CVFAD] Keynote talk by Kristen Grauman, Vision for Fashion
CVPR 2020 Workshop - Paper 7: From Paris to Berlin: Discovering Fashion Style Influences ...
CVPR 2020 Workshop - Paper 6: Cross-Modal Fashion Product Search with Transformer ...
CVPR 2020 Workshop - Keynote: Hui Wu
CVPR 2020 Workshop - Keynote: Kristen Grauman
CVPR 2020 Open Access Repository
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