Computer Vision - ACCV 2020 : 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 - December 4, 2020, Revised Selected Papers, Part III / 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, 12624

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12624

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer engineering.

Computer networks.

Pattern recognition systems.

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Automated Pattern Recognition.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computer Engineering and Networks.

Automated Pattern Recognition.

Physical Description:

1 online resource (XVIII, 757 pages) : 245 illustrations, 229 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:

Recognition and Detection

End-to-end Model-based Gait Recognition

Horizontal Flipping Assisted Disentangled Feature Learning for Semi-Supervised Person Re-Identification

MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings

Backbone Based Feature Enhancement for Object Detection

Long-Term Cloth-Changing Person Re-identification

Any-Shot Object Detection

Background Learnable Cascade for Zero-Shot Object Detection

Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation

COG: COnsistent data auGmentation for object perception

Synthesizing the Unseen for Zero-shot Object Detection

Fully Supervised and Guided Distillation for One-Stage Detectors

Visualizing Color-wise Saliency of Black-Box Image Classification Models

ERIC: Extracting Relations Inferred from Convolutions

D2D: Keypoint Extraction with Describe to Detect Approach

Accurate Arbitrary-Shaped Scene Text Detection via Iterative Polynomial Parameter Regression

Adaptive Spotting: Deep Reinforcement Object Search in 3D Point Clouds

Efficient Large-Scale Semantic Visual Localization in 2D Maps

Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild

Scale-Aware Polar Representation for Arbitrarily-Shaped Text Detection

Branch Interaction Network for Person Re-identification

BLT: Balancing Long-Tailed Datasets with Adversarially-Perturbed Images

Jointly Discriminating and Frequent Visual Representation Mining

Discrete Spatial Importance-Based Deep Weighted Hashing

Low-level Sensor Fusion Network for 3D Vehicle Detection using Radar Range-Azimuth Heatmap and Monocular Image

MLIFeat: Multi-level information fusion based deep local features

CLASS: Cross-Level Attention and Supervision for Salient Objects Detection

Cascaded Transposed Long-range Convolutions for Monocular Depth Estimation

Optimization, Statistical Methods, and Learning

Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks

Large-Scale Cross-Domain Few-Shot Learning

Channel Pruning for Accelerating Convolutional Neural Networks via Wasserstein Metric

Progressive Batching for Efficient Non-linear Least Squares

Fast and Differentiable Message Passing on Pairwise Markov Random Fields

A Calibration Method for the Generalized Imaging Model with Uncertain Calibration Target Coordinates

Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network

Towards Fast and Robust Adversarial Training for Image Classification

Few-Shot Zero-Shot Learning: Knowledge Transfer with Less Supervision

Lossless Image Compression Using a Multi-Scale Progressive Statistical Model

Spatial Class Distribution Shift in Unsupervised Domain Adaptation: Local Alignment Comes to Rescue

Robot Vision

Point Proposal based Instance Segmentation with Rectangular Masks for Robot Picking Task

Multi-task Learning with Future States for Vision-based Autonomous Driving

MTNAS: Search Multi-Task Networks for Autonomous Driving

Compact and Fast Underwater Segmentation Network for Autonomous Underwater Vehicles

L2R GAN: LiDAR-to-Radar Translation

V2A - Vision to Action: Learning robotic arm actions based on vision and language

To Filter Prune, or to Layer Prune, That Is The Question.

Other Format:

Printed edition:

ISBN:

978-3-030-69535-4

9783030695354

Access Restriction:

Restricted for use by site license.