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Computer Vision – ACCV 2020

15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part IV

  • Conference proceedings
  • © 2021

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Editors:
  1. Hiroshi Ishikawa

    1. Waseda University, Tokyo, Japan

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  2. Cheng-Lin Liu

    1. Institute of Automation of Chinese Academy of Sciences, Beijing, China

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  3. Tomas Pajdla

    1. Czech Technical University in Prague, Prague, Czech Republic

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  4. Jianbo Shi

    1. University of Pennsylvania, Philadelphia, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12625)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: ACCV 2020.

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About this book

The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.*

The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:

Part I: 3D computer vision; segmentation and grouping

Part II: low-level vision, image processing; motion and tracking

Part III: recognition and detection; optimization, statistical methods, and learning; robot vision

Part IV: deep learning for computer vision, generative models for computer vision

Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis

Part VI: applications of computer vision; vision for X; datasets and performance analysis

*The conference was held virtually.

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Keywords

Table of contents (42 papers)

  1. Front Matter

    Pages i-xviii
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  2. Deep Learning for Computer Vision

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization

      • Minsong Ki, Youngjung Uh, Wonyoung Lee, Hyeran Byun
      Pages 3-18

    3. Exploiting Transferable Knowledge for Fairness-Aware Image Classification

      • Sunhee Hwang, Sungho Park, Pilhyeon Lee, Seogkyu Jeon, Dohyung Kim, Hyeran Byun
      Pages 19-35

    4. Introspective Learning by Distilling Knowledge from Online Self-explanation

      • Jindong Gu, Zhiliang Wu, Volker Tresp
      Pages 36-52

    5. Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarity

      • Engkarat Techapanurak, Masanori Suganuma, Takayuki Okatani
      Pages 53-69

    6. Meta-Learning with Context-Agnostic Initialisations

      • Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen
      Pages 70-86

    7. Second Order Enhanced Multi-glimpse Attention in Visual Question Answering

      • Qiang Sun, Binghui Xie, Yanwei Fu
      Pages 87-103

    8. Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection

      • Heng Zhang, Elisa Fromont, Sébastien Lefevre, Bruno Avignon
      Pages 104-118

    9. Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes

      • Zhengxi Zhang, Liang Zhao, Yunan Liu, Shanshan Zhang, Jian Yang
      Pages 119-135

    10. Part-Aware Attention Network for Person Re-identification

      • Wangmeng Xiang, Jianqiang Huang, Xian-Sheng Hua, Lei Zhang
      Pages 136-152

    11. Image Captioning Through Image Transformer

      • Sen He, Wentong Liao, Hamed R. Tavakoli, Michael Yang, Bodo Rosenhahn, Nicolas Pugeault
      Pages 153-169

    12. Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Acceleration

      • Junjie He, Bohua Chen, Yinzhang Ding, Dongxiao Li
      Pages 170-186

    13. Learn More, Forget Less: Cues from Human Brain

      • Arijit Patra, Tapabrata Chakraborti
      Pages 187-202

    14. Knowledge Transfer Graph for Deep Collaborative Learning

      • Soma Minami, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
      Pages 203-217

    15. Regularizing Meta-learning via Gradient Dropout

      • Hung-Yu Tseng, Yi-Wen Chen, Yi-Hsuan Tsai, Sifei Liu, Yen-Yu Lin, Ming-Hsuan Yang
      Pages 218-234

    16. Vax-a-Net: Training-Time Defence Against Adversarial Patch Attacks

      • T. Gittings, S. Schneider, J. Collomosse
      Pages 235-251

    17. Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed

      • Dong Li, Sitong Chen, Xudong Liu, Yunda Sun, Li Zhang
      Pages 252-267

    18. Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

      • Shuyang Dai, Yu Cheng, Yizhe Zhang, Zhe Gan, Jingjing Liu, Lawrence Carin
      Pages 268-283

    19. Double Targeted Universal Adversarial Perturbations

      • Philipp Benz, Chaoning Zhang, Tooba Imtiaz, In So Kweon
      Pages 284-300
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Other volumes

  1. Computer Vision – ACCV 2020

  2. Computer Vision – ACCV 2020

  3. Computer Vision – ACCV 2020

  4. Computer Vision – ACCV 2020

  5. Computer Vision – ACCV 2020

  6. Computer Vision – ACCV 2020

  7. Computer Vision – ACCV 2020 Workshops

Editors and Affiliations

  • Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  • Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  • Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  • University of Pennsylvania, Philadelphia, USA

    Jianbo Shi

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