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

Auto-USOD: Searching Topology for Underwater Salient Object Detection.- MBA-NER: Multi-Granularity Entity Boundary-Aware Contrastive Enhanced for Two-stage Few-Shot Named Entity Recognition.- Meta-Learning Based Knowledge Distillation for Domain Adaptive Nighttime Segmentation.- Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts.- Towards Adversarial-Robust Class-Incremental Learning via Progressively Volume-up Perturbation Generation.- Neighborhood Difference-Enhanced Graph Neural Network based on Hypergraph for Social Bot Detection.- SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations.- An Entropy-based Pseudo-Label Mixup Method for Source-Free Domain Adaptation.- DAMS: Document Image Steganography with Dual Attention Multi-Scale Encoder-Decoder Architecture.- Dual-Task Cascaded for Proactive Deepfake Detection Using QPCET watermarking.- XrGroup: Graph Convolutional Networks for Group-Aware Pedestrian Trajectory Prediction with Speed information.- Generative Steganography Based on Dual-Branch Flow.- Invisible Backdoor Attack Through Singular Value Decomposition.- Self-supervised transformer-based pre-training method with General Plant InflCÊ