Computer Vision – ACCV 2020 Workshops

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

AvImari Sato,Bohyung Han

Häftad, Engelska, 2021

Del 12628 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2021-02-24
Mått:155 x 235 x 12 mm
Vikt:324 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:199
Förlag:Springer Nature Switzerland AG
ISBN:9783030697556

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Nätverk och kommunikation inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

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.