cvpaper.challenge の Meta Study Group 発表スライド
cvpaper.challenge はコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有します。2019の目標「トップ会議30+本投稿」「2回以上のトップ会議網羅的サーベイ」
http://xpaperchallenge.org/cv/
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
cvpaper.challenge の Meta Study Group 発表スライド
cvpaper.challenge はコンピュータビジョン分野の今を映し、トレンドを創り出す挑戦です。論文サマリ・アイディア考案・議論・実装・論文投稿に取り組み、凡ゆる知識を共有します。2019の目標「トップ会議30+本投稿」「2回以上のトップ会議網羅的サーベイ」
http://xpaperchallenge.org/cv/
You Only Look One-level Featureの解説と見せかけた物体検出のよもやま話Yusuke Uchida
第7回全日本コンピュータビジョン勉強会「CVPR2021読み会」(前編)の発表資料です
https://kantocv.connpass.com/event/216701/
You Only Look One-level Featureの解説と、YOLO系の雑談や、物体検出における関連する手法等を広く説明しています
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
論文紹介:A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, a...Toru Tamaki
Chaoyang Zhu, Long Chen, "A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future" arXiv2023
https://arxiv.org/abs/2307.09220
本スライドは、弊社の梅本により弊社内の技術勉強会で使用されたものです。
近年注目を集めるアーキテクチャーである「Transformer」の解説スライドとなっております。
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
論文紹介:A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, a...Toru Tamaki
Chaoyang Zhu, Long Chen, "A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future" arXiv2023
https://arxiv.org/abs/2307.09220
論文紹介:Multi-criteria Token Fusion with One-step-ahead Attention for Efficient ...Toru Tamaki
Sanghyeok Lee, Joonmyung Choi, Hyunwoo J. Kim, "Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers" arXiv2024
https://arxiv.org/abs/2403.10030
論文紹介:ArcFace: Additive Angular Margin Loss for Deep Face RecognitionToru Tamaki
Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou , "ArcFace: Additive Angular Margin Loss for Deep Face Recognition" CVPR2019
https://openaccess.thecvf.com/content_CVPR_2019/html/Deng_ArcFace_Additive_Angular_Margin_Loss_for_Deep_Face_Recognition_CVPR_2019_paper.html
論文紹介:Deep Occlusion-Aware Instance Segmentation With Overlapping BiLayersToru Tamaki
Lei Ke, Yu-Wing Tai, Chi-Keung Tang, "Deep Occlusion-Aware Instance Segmentation With Overlapping BiLayers" CVPR2021
https://openaccess.thecvf.com/content/CVPR2021/html/Ke_Deep_Occlusion-Aware_Instance_Segmentation_With_Overlapping_BiLayers_CVPR_2021_paper.html
論文紹介:Automated Classification of Model Errors on ImageNetToru Tamaki
Momchil Peychev, Mark Müller, Marc Fischer, Martin Vechev, " Automated Classification of Model Errors on ImageNet", NeurIPS2023
https://proceedings.neurips.cc/paper_files/paper/2023/hash/7480ed13740773505262791131c12b89-Abstract-Conference.html
論文紹介:MOSE: A New Dataset for Video Object Segmentation in Complex ScenesToru Tamaki
Henghui Ding, Chang Liu, Shuting He, Xudong Jiang, Philip H.S. Torr, Song Bai, " MOSE: A New Dataset for Video Object Segmentation in Complex Scenes " ICCV2023
論文紹介:Tracking Anything with Decoupled Video SegmentationToru Tamaki
Ho Kei Cheng, Seoung Wug Oh, Brian Price, Alexander Schwing, Joon-Young Lee, " Tracking Anything with Decoupled Video Segmentation " ICCV2023
https://openaccess.thecvf.com/content/ICCV2023/html/Cheng_Tracking_Anything_with_Decoupled_Video_Segmentation_ICCV_2023_paper.html
論文紹介:Real-Time Evaluation in Online Continual Learning: A New HopeToru Tamaki
Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip H.S. Torr, Bernard Ghanem, " Real-Time Evaluation in Online Continual Learning: A New Hope " CVPR2023
https://openaccess.thecvf.com/content/CVPR2023/html/Ghunaim_Real-Time_Evaluation_in_Online_Continual_Learning_A_New_Hope_CVPR_2023_paper.html
論文紹介:PointNet: Deep Learning on Point Sets for 3D Classification and Segmenta...Toru Tamaki
Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas, " PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation " CVPR2017
https://openaccess.thecvf.com/content_cvpr_2017/html/Qi_PointNet_Deep_Learning_CVPR_2017_paper.html
24. Unsupervised I2I Single-modal Output
◼Training with multi-modules
• 2 I2I
•
• Domain-Bank [Hui+, ICPR2018]
• n
n
• ModularGAN [Zhao+, ECCV2018]
•
25. Training with one generator and discriminator pair
◼Training with one generator and discriminator pair
•
•
• StarGAN [Choi+, CVPR2018]
•
•
• AttGAN [He+, IEEE TIP2019]
•
•
• RelGAN [Wu+, ICCV2019] STGAN [Liu+, CVPR2019]
•
•
StarGAN, RelGAN
26. Training by combining knowledge in other fields
◼Training by combining knowledge in other fields
•
• Fixed-Point GAN [Siddiquee+, ICCV2019]
•
• SGN [Chang+, ICCV2019]
•
sym-parameter
• INIT [Cao+, ECCV2020]
•
• ADSPM [Wu+, ICCV2019]
•
SGN Structure