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Artificial Neural Networks and Machine Learning – ICANN 2021

30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part II

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Igor Farkaš

    1. Comenius University in Bratislava, Bratislava, Slovakia

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  2. Paolo Masulli

    1. iMotions A/S, Copenhagen, Denmark

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  3. Sebastian Otte

    1. University of Tübingen, Tübingen, Germany

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  4. Stefan Wermter

    1. Universität Hamburg, Hamburg, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12892)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: ICANN 2021.

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Table of contents (51 papers)

  1. Front Matter

    Pages i-xxiii
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  2. Computer Vision and Object Detection

    1. Front Matter

      Pages 1-1
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    2. Selective Multi-scale Learning for Object Detection

      • Junliang Chen, Weizeng Lu, Linlin Shen
      Pages 3-14

    3. DRENet: Giving Full Scope to Detection and Regression-Based Estimation for Video Crowd Counting

      • Changsheng Liu, Yuan Huang, Yadong Mu, Xiaoming Yu
      Pages 15-27

    4. Sisfrutos Papaya: A Dataset for Detection and Classification of Diseases in Papaya

      • Jairo Lucas de Moraes, Jorcy de Oliveira Neto, Jacson R. Correia-Silva, Thiago M. Paixão, Claudine Badue, Thiago Oliveira-Santos et al.
      Pages 28-39

    5. Faster-LTN: A Neuro-Symbolic, End-to-End Object Detection Architecture

      • Francesco Manigrasso, Filomeno Davide Miro, Lia Morra, Fabrizio Lamberti
      Pages 40-52

    6. GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting

      • Ying Shi, Jun Sang, Jinghan Tan, Zhongyuan Wu, Bin Cai, Nong Sang
      Pages 53-66

    7. Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a Single Depth View

      • Caixia Liu, Dehui Kong, Shaofan Wang, Jinghua Li, Baocai Yin
      Pages 67-79

    8. Facial Expression Recognition by Expression-Specific Representation Swapping

      • Jie Lei, Zhao Liu, Zeyu Zou, Tong Li, Juan Xu, Zunlei Feng et al.
      Pages 80-91

    9. Iterative Error Removal for Time-of-Flight Depth Imaging

      • Zhuolin Zheng, Yinzhang Ding, Xiaotian Tang, Yu Cai, Dongxiao Li, Ming Zhang et al.
      Pages 92-105

    10. Blurred Image Recognition: A Joint Motion Deblurring and Classification Loss-Aware Approach

      • Wenjie Zhang, Zhi Wang
      Pages 106-117

    11. Learning How to Zoom In: Weakly Supervised ROI-Based-DAM for Fine-Grained Visual Classification

      • Wenjie Chen, Shuang Ran, Tian Wang, Lihong Cao
      Pages 118-130

  3. Convolutional Neural Networks and Kernel Methods

    1. Front Matter

      Pages 131-131
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    2. (Input) Size Matters for CNN Classifiers

      • Mats L. Richter, Wolf Byttner, Ulf Krumnack, Anna Wiedenroth, Ludwig Schallner, Justin Shenk
      Pages 133-144

    3. Accelerating Depthwise Separable Convolutions with Vector Processor

      • Yuekai Zhao, Jianzhuang Lu, Xiaowen Chen
      Pages 145-156

    4. KCNet: Kernel-Based Canonicalization Network for Entities in Recruitment Domain

      • Nidhi Goyal, Niharika Sachdeva, Anmol Goel, Jushaan Singh Kalra, Ponnurangam Kumaraguru
      Pages 157-169

    5. Deep Unitary Convolutional Neural Networks

      • Hao-Yuan Chang, Kang L. Wang
      Pages 170-181

  4. Deep Learning and Optimization I

    1. Front Matter

      Pages 183-183
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    2. DPWTE: A Deep Learning Approach to Survival Analysis Using a Parsimonious Mixture of Weibull Distributions

      • Achraf Bennis, Sandrine Mouysset, Mathieu Serrurier
      Pages 185-196

    3. First-Order and Second-Order Variants of the Gradient Descent in a Unified Framework

      • Thomas Pierrot, Nicolas Perrin-Gilbert, Olivier Sigaud
      Pages 197-208
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Other volumes

  1. Artificial Neural Networks and Machine Learning – ICANN 2021

  2. Artificial Neural Networks and Machine Learning – ICANN 2021

  3. Artificial Neural Networks and Machine Learning – ICANN 2021

  4. Artificial Neural Networks and Machine Learning – ICANN 2021

  5. Artificial Neural Networks and Machine Learning – ICANN 2021

Keywords

About this book

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes.


In this volume, the papers focus on topics such as computer vision and object detection, convolutional neural networks and kernel methods, deep learning and optimization, distributed and continual learning, explainable methods, few-shot learning and generative adversarial networks.

*The conference was held online 2021 due to the COVID-19 pandemic.

Editors and Affiliations

  • Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  • iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  • University of Tübingen, Tübingen, Germany

    Sebastian Otte

  • Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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