Computer Vision - ACCV 2020 Workshops

15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, Revised Selected Papers
1. Auflage 2021
ISBN: 978-3-030-69755-6
Verlag: Springer International Publishing

Buch, Englisch, Band 12628, 199 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 324 g

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Han, Bohyung

Sato, Imari

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

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.