Pattern Recognition and Computer Vision
7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part I
Series: Lecture Notes in Computer Science; 15031;
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Product details:
- Edition number 2024
- Publisher Springer Nature Singapore
- Date of Publication 4 November 2024
- Number of Volumes 1 pieces, Book
- ISBN 9789819784868
- Binding Paperback
- No. of pages569 pages
- Size 235x155 mm
- Language English
- Illustrations XIV, 569 p. 155 illus., 149 illus. in color. Illustrations, black & white 609
Categories
Long description:
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 18–20, 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.
Table of Contents:
Cluster center initialization for fuzzy K-modes clustering using outlier detection technique.- Few-Shot Class-Incremental Learning via Cross-Modal Alignment with Feature Replay.- Generalizing soft actor-critic algorithms to discrete action spaces.- LarvSeg: Exploring Image Classification Data For Large Vocabulary Semantic Segmentation via Category-wise Attentive Classifier.- Exploring Out-of-distribution Scene Text Recognition for Driving Scenes with Hybrid Test-time Adaptation.- PhaseNN: An Unsupervised and Spatial-Frequency Integrated Network for Phase Retrieval.- Sequential Transfer of Pose and Texture for Pose Guided Person Image Generation.- Balanced Clustering with Discretely Weighted Pseudo-Label.- Tensor Robust Principal Component Analysis with Hankel Structure.- Self-Distillation via Intra-class Compactness.- An Enhanced Dual-Channel-Omni-Scale 1DCNN for Fault Diagnosis.- Visual-Guided Reasoning Path Generation for Visual Question Answering.- FedGC: Federated Learning on Non-IID Data via Learning from Good Clients.- Inter-class Correlation-based Online Knowledge Distillation.- Accelerating Domain Adaptation with Cascaded Adaptive Vision Transformer.- Multistage Compression Optimization Strategies for Accelerating Diffusion Models.- Defending Adversarial Patches via Joint Region Localizing and Inpainting.- Multi-view Spectral Clustering Based on Topological Manifold Learning.- Client selection mechanism for federated learning based on class imbalance.- A New Paradigm for Enhancing Ensemble Learning through Parameter Diversification.- Adaptive Multi-Information Feature Fusion MLP with Filter Enhancement for Sequential Recommendation.- FedDCP: Personalized Federated Learning Based on Dual Classifiers and Prototypes.- AtomTool: Empowering Large Language Models with Tool Utilization Skills.- Making the Primary Task Primary: Boosting Few-Shot Classification by Gradient-biased Multi-task Learning.- Cascade Large Language Model via In-Context Learning for Depression Detection on Chinese Social Media.- TRAE : Reversible Adversarial Example with Traceability.- A Two-stage Active Domain Adaptation Framework for Vehicle Re-Identification.- FBR-FL: Fair and Byzantine-Robust Federated Learning via SPD Manifold.- SecBFL-IoV: A Secure Blockchain-Enabled Federated Learning Framework for Resilience against Poisoning Attacks in Internet of Vehicles.- Adapt and Refine: A Few-Shot Class-Incremental Learner via Pre-trained Models.- Learning Fully Parametric Subspace Clustering.- A Comprehensive Exploration on Detecting Fake Images Generated by Stable Diffusion.- Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental Learning.- SACTGAN-EE imbalanced data processing method for credit default prediction.- FedHC: Learning Imbalanced Clusters via Federated Hierarchical Clustering.- Enhancing Time Series Classification with Explainable Time-frequency Features Representation.- Adaptive Unified Framework with Global Anchor Graph for Large-scale Multi-view Clustering.- SLRL: Structured Latent Representation Learning for Multi-view Clustering.
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