Pattern Recognition and Computer Vision

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.

Contenu

Scale-Adaptive Modulation Meet Compact Axial Transformer for Small Object Detection in UAV-Vision.- Lightweight and Multi-Scale Adaptive Network for Infrared Small Target Detection.- Multi-View Cross-Attention Network for Hyperspectral Object Tracking.- CountMamba: Exploring Multi-directional Selective State-Space Models for Plant Counting.- ECLNet: A Compact Encoder-Decoder Network for Efficient Camouflaged Object Detection.- Few-Shot Object Detection via Disentangling Class-Related Factors in Feature Distribution.- Multi-class token-guided end-to-end weakly supervised image semantic segmentation method.- Dynamic Subframe Splitting and Spatio-Temporal Motion Entangled Sparse Attention for RGB-E Tracking.- DIDNet: An End-to-End Directional Insulator Detection Network based on direction field.- L2FIG-Tracker: l2-norm based Fusion with Illumination Guidance for RGB-D Object TrackingCompleting Saliency from Details.- CDAF3D: Cross-Dimensional Attention Fusion for Indoor 3D Object Detection.- RETrack: Multi-Object Tracking by Associating Proposal Regions.- PGNET: A Real-time efficient model for underwater object detectionA Temporal Recognition Framework for Multi-Sheep Behaviour Using ViTSORT and YOLOv8-MS.- Tracking Transforming Objects: A Benchmark.- Modality-Shared Prototypes for Enhanced Unsupervised Visible-Infrared Person Re-identification.- Vehicle Re-identification with a Pose-aware Discriminative Part Learning Model.- Dual-Teacher Network with SSIM based Reverse Distillation for Anomaly DetectionCFMVOR: Federated Multi-view 3D Object Recognition Based on Compressed Learning.- Enhanced Anomaly Detection using Spatial-Alignment and Multi-scale Fusion.- Confidence-Weighted Teacher: Semi-Supervised Object Detection Based on Confidence Correction.- Shape-Aware Soft Label Assignment and Context Enhancement for Oriented Object Detection.- Chareption: Change-Aware Adaption Empowers Large Language Model for Effective Remote Sensing Image Change Captioning.- Spectral-Spatial Multi-view Sparse Self-Representation for Hyperspectral Band Selection.- Adaptive Cross-spatial Sensing Network for Change Detection.- HANet: Hierarchical Attention Network for Remote Sensing Images Semantic Segmentation.- BiReNet: Bilateral Network with Feature Fusion and Edge Detection for Remote Sensing Images Road Extraction.- Latent Feature Representation-Based Low Rank Subspace Clustering for Hyperspectral Band Selection.- DICMNet: Dynamic Irregular Resnet with Multi-direction Channel Remapping for Remote Sensing Road Extraction.- Discriminative Representation-based Classifier for Few-shot Remote Sensing Classification.- Hyperspectral Image Change Detection via Cross-Sample Slot Attention and Dual Gated Feed-Forward Network.- Hyperspectral Image Super-resolution Based on Dual-domain Gated Attention Network.- Spectral Channel-weighting CAT for Hyperspectral image ClassificationBFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation.- SFFAFormer: An Semantic Fusion and Feature Accumulation Approach for Change Detection on Remote Sensing ImagesA Novel Multi-scale Feature Fusion based Network for Hyperspectral and Multispectral Image Fusion.- A Sidelobe-Aware Semi-Deformable Convolutional Ship Detection Network for Synthetic Aperture Radar Imagery.- Feature Exchange and Distribution-based Mining Land Detection Method by Multispectral Imagery.