Computer Vision - ACCV 2020 : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, Revised Selected Papers, Part IV / edited by Hiroshi Ishikawa, Cheng-Lin Liu, Tomas Pajdla, Jianbo Shi.

Format:

Book

Contributor:

Ishikawa, Hiroshi, Editor.

Liu, Cheng-Lin, Editor.

Pajdla, Tomáš, Editor.

Shi, Jianbo, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12625

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12625

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer engineering.

Computer networks.

Pattern recognition systems.

Application software.

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Automated Pattern Recognition.

Computer and Information Systems Applications.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Automated Pattern Recognition.

Computer and Information Systems Applications.

Physical Description:

1 online resource (XVIII, 715 pages) : 284 illustrations, 278 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

Deep Learning for Computer Vision

In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization

Exploiting Transferable Knowledge for Fairness-aware Image Classification

Introspective Learning by Distilling Knowledge from Online Self-explanation

Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarity

Meta-Learning with Context-Agnostic Initialisations

Second Order enhanced Multi-glimpse Attention in Visual Question Answering

Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection

Unified Density-Aware Image Dehazing and Object Detection in Real-World Hazy Scenes

Part-aware Attention Network for Person Re-Identification

Image Captioning through Image Transformer

Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Acceleration

Learn more, forget less: Cues from human brain

Knowledge Transfer Graph for Deep Collaborative Learning

Regularizing Meta-Learning via Gradient Dropout

Vax-a-Net: Training-time Defence Against Adversarial Patch Attacks

Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed

Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation

Double Targeted Universal Adversarial Perturbations

Adversarially Robust Deep Image Super-Resolution using Entropy Regularization

Online Knowledge Distillation via Multi-branch Diversity Enhancement

Rotation Equivariant Orientation Estimation for Omnidirectional Localization

Contextual Semantic Interpretability

Few-Shot Object Detection by Second-order Pooling

Depth-Adapted CNN for RGB-D cameras

Generative Models for Computer Vision

Over-exposure Correction via Exposure and Scene Information Disentanglement

Novel-View Human Action Synthesis

Augmentation Network for Generalised Zero-Shot Learning

Local Facial Makeup Transfer via Disentangled Representation

OpenGAN: Open Set Generative Adversarial Networks

CPTNet: Cascade Pose Transform Network for Single Image Talking Head Animation

TinyGAN: Distilling BigGAN for Conditional Image Generation

A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings

RF-GAN: A Light and Reconfigurable Network for Unpaired Image-to-Image Translation

GAN-based Noise Model for Denoising Real Images

Emotional Landscape Image Generation Using Generative Adversarial Networks

Feedback Recurrent Autoencoder for Video Compression

MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network

DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

dpVAEs: Fixing Sample Generation for Regularized VAEs

MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network

EvolGAN: Evolutionary Generative Adversarial Networks

Sequential View Synthesis with Transformer.

Other Format:

Printed edition:

ISBN:

978-3-030-69538-5

9783030695385

Access Restriction:

Restricted for use by site license.