Computer Vision - ACCV 2020: 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, Revised Selected Papers, Part II

by Hiroshi Ishikawa (Editor), Cheng-Lin Liu (Editor), Tomas Pajdla (Editor), Jianbo Shi (Editor)

Overview

The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.*

The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:

Part I: 3D computer vision; segmentation and grouping

Part II: low-level vision, image processing; motion and tracking

Part III: recognition and detection; optimization, statistical methods, and learning; robot vision

Part IV: deep learning for computer vision, generative models for computer vision

Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis

Part VI: applications of computer vision; vision for X; datasets and performance analysis

*The conference was held virtually.

Product Details

ISBN-13:	9783030695323
Publisher:	Springer International Publishing
Publication date:	02/26/2021
Series:	Lecture Notes in Computer Science , #12623
Sold by:	Barnes & Noble
Format:	eBook
File size:	218 MB
Note:	This product may take a few minutes to download.

Table of Contents

Low-Level Vision, Image Processing.- Image Inpainting with Onion Convolutions.- Accurate and Efficient Single Image Super-Resolution with Matrix Channel Attention Network.- Second-order Camera-aware Color Transformation for Cross-domain Person Re-identification.- CS-MCNet:A Video Compressive Sensing Reconstruction Network with Interpretable Motion Compensation.- MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining.- Degradation Model Learning for Real-World Single Image Super-resolution.- Chromatic Aberration Correction Using Cross-Channel Prior in Shearlet Domain.- Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment.- Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallax.- Low-light Color Imaging via Dual Camera Acquisition.- Frequency Attention Network: Blind Noise Removal for Real Images.- Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network.- Color Enhancement using Global Parameters and Local Features Learning.- An Efficient Group Feature Fusion Residual Network for Image Super-Resolution.- Adversarial Image Composition with Auxiliary Illumination.- Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network.- Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning.- Multi-scale Attentive Residual Dense Network for Single Image Rain Removal.- FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization.- Human Motion Deblurring using Localized Body Prior.- Synergistic Saliency and Depth Prediction for RGB-D Saliency Detection.- Deep Snapshot HDR Imaging Using Multi-Exposure Color Filter Array.- Deep Priors inside an Unrolled and Adaptive Deconvolution Model.- Motion and Tracking.- Adaptive Spatio-Temporal Regularized Correlation Filters for UAV-based Tracking.- Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation.- Self-supervised Sparse to Dense Motion Segmentation.- Recursive Bayesian Filtering for Multiple Human Pose Tracking from Multiple Cameras.- Adversarial Refinement Network for Human Motion Prediction.- Semantic Synthesis of Pedestrian Locomotion.- Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation.- Visual Tracking by TridentAlign and Context Embedding.- Leveraging Tacit Information Embedded in CNN Layers for Visual Tracking.- A Two-Stage Minimum Cost Multicut Approach to Self-Supervised Multiple Person Tracking.- Learning Local Feature Descriptors for Multiple Object Tracking.- VAN: Versatile Affinity Network for End-to-end Online Multi-Object Tracking.- COMET: Context-Aware IoU-Guided Network for Small Object Tracking.- Adversarial Semi-Supervised Multi-Domain Tracking.- Tracking-by-Trackers with a Distilled and Reinforced Model.- Motion Prediction Using Temporal Inception Module.- A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking.- Modeling Cross-Modal interaction in a Multi-detector, Multi-modal Tracking Framework.- Dense Pixel-wise Micro-motion Estimation of Object Surface by using Low Dimensional Embedding of Laser Speckle Pattern.