Pattern Recognition and Computer Vision : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part I / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji.

Format:

Book

Contributor:

Liu, Qingshan, Editor.

Wang, Hanzi., Editor.

Ma, Zhanyu., Editor.

Zheng, Weishi., Editor.

Zha, Hongbin, Editor.

Chen, Xilin, Editor.

Wang, Liang, Editor.

Ji, Rongrong, Editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 14425

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Artificial intelligence.

Application software.

Computer networks.

Computer systems.

Machine learning.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer and Information Systems Applications.

Computer Communication Networks.

Computer System Implementation.

Machine Learning.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computer and Information Systems Applications.

Computer Communication Networks.

Computer System Implementation.

Machine Learning.

Physical Description:

1 online resource (XIV, 513 p. 159 illus., 152 illus. in color.)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications andSystems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis. .

Contents:

Intro

Preface

Organization

Contents - Part I

Action Recognition

Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion Based Classification

1 Introduction

2 Related Work

3 Our Proposed Approach

3.1 Overview

3.2 Network Architecture

4 Experiment

4.1 Dataset and Evaluation Metric

4.2 Implementation Details

4.3 Comparison with Other SOTA Algorithms

4.4 Ablation Study

4.5 Parameter Analysis

5 Conclusion

References

Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition

2 Related Works

2.1 Attention Mechanism

2.2 Lightweight Models

3 Method

3.1 Multi-Branch Fusion Module

3.2 Semantic Information

3.3 Graph Convolution Module

3.4 Time Convolution Module

4.1 Dataset

4.2 Experimental Details

4.3 Ablation Experiment

4.4 Comparison with State-of-the-Art

5 Action Visualization

6 Conclusion

Auto-Learning-GCN: An Ingenious Framework for Skeleton-Based Action Recognition

3 Methodology

3.1 GCN-Based Skeleton Processing

3.2 The AL-GCN Module

3.3 The Attention Correction and Jump Model

3.4 Multi-stream Gaussian Weight Selection Algorithm

4 Experimental Results and Analysis

4.1 Datasets

4.3 Compared with the State-of-the-Art Methods

4.5 Visualization

Skeleton-Based Action Recognition with Combined Part-Wise Topology Graph Convolutional Networks

2.1 Skeleton-Based Action Recognition

2.2 Partial Graph Convolution in Skeleton-Based Action Recognition

3 Methods

3.1 Preliminaries

3.2 Part-Wise Spatial Modeling

3.3 Part-Wise Spatio-Temporal Modeling.

3.4 Model Architecture

4 Experiments

4.2 Training Details

4.3 Ablation Studies

4.4 Comparison with the State-of-the-Art

Segmenting Key Clues to Induce Human-Object Interaction Detection

3 Approach

3.1 Key Features Segmentation-Based Module

3.2 Key Features Learning Encoder

3.3 Spatial Relationships Learning Graph-Based Module

3.4 Training and Inference

4.1 Implementation Details

4.2 Implementation Results

4.3 Ablation Study

4.4 Qualitative Results

Lightweight Multispectral Skeleton and Multi-stream Graph Attention Networks for Enhanced Action Prediction with Multiple Modalities

2.2 Dynamic Graph Neural Network

3.1 Spatial Embedding Component

3.2 Temporal Embedding Component

3.3 Action Prediction

4 Experiments and Discussion

4.1 NTU RGB+D Dataset

4.2 Experiments Setting

4.3 Evaluation of Human Action Recognition

Spatio-Temporal Self-supervision for Few-Shot Action Recognition

2.1 Few-Shot Action Recognition

2.2 Self-supervised Learning (SSL)-Based Few-Shot Learning

3.1 Problem Definition

3.2 Spatio-Temporal Self-supervision Framework

4.1 Experimental Settings

4.2 Comparison with State-of-the-Art Methods

5 Conclusions

A Fuzzy Error Based Fine-Tune Method for Spatio-Temporal Recognition Model

2.1 Spatio-Temporal (3D) Convolution Networks

2.2 Clips Selection and Features Aggregation

3 Proposed Method

3.1 Problem Definition.

3.2 Fuzzy Target

3.3 Fine Tune Loss Function

4.1 Datasets and Implementation Details

4.2 Performance Comparison

4.3 Discussion

Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition

2 Proposed Method

2.1 Network Architecture

2.2 Temporal-Channel Focus Module

2.3 Dynamic Channel Topology Attention Module

3 Experiments

3.1 Datasets and Implementation Details

3.2 Ablation Study

3.3 Comparison with the State-of-the-Art

4 Conclusion

HFGCN-Based Action Recognition System for Figure Skating

2 Figure Skating Hierarchical Dataset

3 Figure Skating Action Recognition System

3.1 Data Preprocessing

3.2 Multi-stream Generation

3.3 Hierarchical Fine-Grained Graph Convolutional Neural Network (HFGCN)

3.4 Decision Fusion Module

4 Experiments and Results

4.1 Experimental Environment

4.2 Experiment Results and Analysis

Multi-modal Information Processing

Image Priors Assisted Pre-training for Point Cloud Shape Analysis

2.1 Problem Setting

2.2 Overview Framework

2.3 Multi-task Cross-Modal SSL

2.4 Objective Function

3 Experiments and Analysis

3.1 Pre-training Setup

3.2 Downstream Tasks

3.3 Ablation Study

AMM-GAN: Attribute-Matching Memory for Person Text-to-Image Generation

2.1 Text-to-image Generative Adversarial Network

2.2 GANs for Person Image

3.1 Feature Extraction

3.2 Multi-scale Feature Fusion Generator

3.3 Real-Result-Driven Discriminator

3.4 Objective Functions

4.2 Implementation

4.3 Evaluation Metrics

4.4 Quantitative Evaluation.

4.5 Qualitative Evaluation

4.6 Ablation Study

RecFormer: Recurrent Multi-modal Transformer with History-Aware Contrastive Learning for Visual Dialog

3.2 Model Architecture

3.3 Training Objectives

4 Experimental Setup

4.2 Baselines

4.3 Evaluation Metric

4.4 Implementation Details

5 Results and Analysis

5.1 Main Results

5.2 Ablation Study

5.3 Attention Visualization

KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing

2 Background

2.1 Text-to-Image Generation and Editing

2.2 Stable Diffusion Model

3 KV Inversion: Training-Free KV Embeddings Learning

3.1 Task Setting and Reason of Existing Problem

3.2 KV Inversion Overview

4.1 Comparisons with Other Concurrent Works

4.2 Ablation Study

5 Limitations and Conclusion

Enhancing Text-Image Person Retrieval Through Nuances Varied Sample

2 Relataed Work

2.1 Text-Image Retrieval

2.2 Text-Image Person Retrieval

3.1 Feature Extraction and Alignment

3.2 Nuanced Variation Module

3.3 Image Text Matching Loss

3.4 Hard Negative Metric Loss

4.1 Datasets and Evaluation Setting

Unsupervised Prototype Adapter for Vision-Language Models

2.1 Large-Scale Pre-trained Vision-Language Models

2.2 Adaptation Methods for Vision-Language Models

2.3 Self-training with Pseudo-Labeling

3.1 Background

3.2 Unsupervised Prototype Adapter

4.1 Image Recognition

4.2 Domain Generalization.

4.3 Ablation Study

Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval

3.2 MCD: Multimodal Causal Discovery

3.3 MMC-CLIP

3.4 Image-Text Alignment

4.1 Datasets and Settings

4.2 Results on MSCOCO

4.3 Results on Flickr30K

4.4 Ablation Studies

Exploring Cross-Modal Inconsistency in Entities and Emotions for Multimodal Fake News Detection

2.1 Single-Modality Fake News Detection

2.2 Multimodal Fake News Detection

3.2 Cross-Modal Contrastive Learning

3.3 Entity Consistency Learning

3.4 Emotional Consistency Learning

3.5 Multimodal Fake News Detector

4.1 Experimental Configurations

4.2 Overall Performance

Deep Consistency Preserving Network for Unsupervised Cross-Modal Hashing

2 The Proposed Method

2.1 Problem Definition

2.2 Deep Feature Extraction and Hashing Learning

2.3 Features Fusion and Similarity Matrix Construction

2.4 Hash Code Fusion and Reconstruction

2.5 Objective Function

3.1 Datasets and Baselines

3.2 Implementation Details

3.3 Results and Analysis

Learning Adapters for Text-Guided Portrait Stylization with Pretrained Diffusion Models

2.1 Text-to-Image Diffusion Models

2.2 Control of Pretrained Diffusion Model

2.3 Text-Guided Portrait Stylizing

3.1 Background and Preliminaries

3.2 Overview of Our Method

3.3 Portrait Stylization with Text Prompt

3.4 Convolution Adapter

3.5 Adapter Optimization

4 Experiments.

4.1 Implementation Settings.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789819984299

9819984297

OCLC:

1416340886