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Computer Vision - ACCV 2020 Workshops de Imari Sato

Computer Vision - ACCV 2020 Workshops: Lecture Notes in Computer Science, cartea 12628

Editat de Imari Sato, Bohyung Han
en Limba Engleză Paperback – 24 feb 2021
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.
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Specificații

ISBN-13: 9783030697556
ISBN-10: 303069755X
Pagini: 208
Ilustrații: VIII, 199 p. 79 illus., 68 illus. in color.
Dimensiuni: 155 x 235 x 12 mm
Greutate: 0.32 kg
Ediția:1st edition 2021
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.