E-grāmata: Pattern Recognition and Computer Vision: 7th Chinese Conference, PRCV 2024, Urumqi, China, October 18-20, 2024, Proceedings, Part I

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.