Computer vision -- ACCV 2020 workshops : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, revised selected papers
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書誌事項
Computer vision -- ACCV 2020 workshops : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, revised selected papers
（Lecture notes in computer science, 12628 . LNCS sublibrary ; SL 6 . Image processing,
Springer, c2021
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ACCV 2020 workshops
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注記
"ACCV 2020 was a fully virtual meeting hosted during the COVID-19 pandemic"--Preface
Includes bibliographical references and author index
内容説明・目次
内容説明
This book constitutes the refereed post-conference proceedings of four workshops held at the 15th Asian Conference on Computer Vision, ACCV 2020, which was held in Kyoto, Japan, in November/ December 2020.*The 13 papers were carefully reviewed and selected from the following two workshops: Machine Learning and Computing for Visual Semantic Analysis (MLCSA) and Multi-Visual-Modality Human Activity Understanding (MMHAU).
*The conference and workshops were held virtually.
目次
Spatial and Channel Attention Modulated Network for Medical Image Segmentation.- Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution.- Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priors.- G-GCSN: Global Graph Convolution Shrinkage Network for Emotion Perception from Gait.- Cell Detection and Segmentation in Microscopy Images with Improved Mask R-CNN.- BdSL36: A Dataset for Bangladeshi Sign Letters Recognition.- 3D Semantic Segmentation for Large-Scale Scene Understanding.- A Weakly Supervised Convolutional Network for Change Segmentation and Classification.- Visible and Thermal Camera-based Jaywalking Estimation using a Hierarchical Deep Learning Framework.- Towards Locality Similarity Preserving to 3D Human Pose Estimation.- Iterative Self-distillation for Precise Facial Landmark Localization.- Multiview Similarity Learning for Robust Visual Clustering.- Real-time Spatio-temporal Action Localization via Learning Motion Representation.
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