Pattern Recognition and Computer Vision: 7th Chinese Conference, PRCV 2024, Urumqi, China, October 1820, 2024, Proceedings, Part IV [Paperback]

This 15-volume set LNCS 15031-15045 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2024, held in Urumqi, China, during October 1820, 2024.
The 579 full papers presented were carefully reviewed and selected from 1526 submissions. The papers cover various topics in the broad areas of pattern recognition and computer vision, including machine learning, pattern classification and cluster analysis, neural network and deep learning, low-level vision and image processing, object detection and recognition, 3D vision and reconstruction, action recognition, video analysis and understanding, document analysis and recognition, biometrics, medical image analysis, and various applications.

Contextual Feature-Based Medical Visual Question Answering Aided by Learnable Matrix.- ImgQuant: Towards adversarial defense with robust boundary via dual-image quantization.- Swelling-ViT: Rethink Data-efficient Vision Transformer from Locality.- Target-Specific Domain Adaptation via Geometry-Correlation Prediction for Point Cloud.- Dual-stream Network of Vision Mamba and CNN with Auto-scaling for Remote Sensing Image Segmentation.- PRM: A Pixel-Region-Matching Approach for Fast Video Object Segmentation.- A Novel Combined GAN for Defects Generation using Masking Mechanisms.- Semi-supervised lightweight fabric defect detection.- Semi-adaptive Synergetic Two-way Pseudoinverse Learning System.- Invariant Risk Minimization Augmentation for Graph Contrastive Learning.- Enhancing Fast Adversarial Training with Learnable Adversarial Perturbations.- DTAFORMER: Directional Time Attention Transformer For Long-Term Series Forecasting.- Unpaired Multi-scenario Sketch Synthesis via Texture Enhancement.- ISO-VTON: Fine-Grained Style-Local Flows with Dual Cross-Attention for Immersive Outfitting.- Near-surface Air Temperature Inversion Study Based on U-Net Family with Multi-source Data.- Relation Detectló%