Pattern recognition and computer vision Part 3
Loss Filtering Factor for Crowd Counting.-Classifier Decoupled Training for Black-Box Unsupervised Domain Adaptation.-Unsupervised Concept Drift Detection via Imbalanced Cluster Discriminator Learning.-Unsupervised Domain Adaptation for Optical Flow Estimation.-Continuous Exploration via Multiple Pe...
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| Sprache: | eng |
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Singapore
Springer
2024
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| Schriftenreihe: | Lecture notes in computer science
14427 |
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> COMPUTERS / Computer Vision & Pattern Recognition
> COMPUTERS / Data Processing / General
> COMPUTERS / Hardware / Network Hardware
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> Computer vision
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| Zusammenfassung: | Loss Filtering Factor for Crowd Counting.-Classifier Decoupled Training for Black-Box Unsupervised Domain Adaptation.-Unsupervised Concept Drift Detection via Imbalanced Cluster Discriminator Learning.-Unsupervised Domain Adaptation for Optical Flow Estimation.-Continuous Exploration via Multiple Perspectives in Sparse Reward Environment.-Network Transplanting for the Functionally Modular Architecture.-TiAM-GAN: Titanium Alloy Microstructure Image Generation Network.-A Robust Detection and Correction Framework for GNN-based Vertical Federated Learning.-QEA-Net: Quantum-Effects-based Attention Networks.-Learning Scene Graph for Better Cross-Domain Image Captioning.-Enhancing Rule Learning on Knowledge Graphs through Joint Ontology and Instance Guidance.-Explore Across-Dimensional Feature Correlations for Few-Shot Learning.-Pairwise-emotion Data Distribution Smoothing for Emotion Recognition.-SIEFusion: Infrared and Visible Image Fusion via Semantic Information Enhancement.-DeepChrom: A Diffusion-Based Framework for Long-Tailed Chromatin State Prediction.-Adaptable Conservative Q-Learning for Offline Reinforcement Learning.-Boosting Out-of-Distribution Detection with Sample Weighting.-Causal discovery via the subsample based reward and punishment mechanism.-Local Neighbor Propagation Embedding.-Inter-class sparsity based non-negative transition sub-space learning.-Incremental Learning Based on Dual-branch Network.-Inter-Image Discrepancy Knowledge Distillation for Semantic Segmentation.-Cascaded Bilinear Mapping Collaborative Hybrid Attention Modality Fusion Model.-CasFormer: Cascaded Transformer based on Dynamic Voxel Pyramid for 3D Object Detection from Point Clouds.-Generalizable and Accurate 6D Object Pose Estimation Network.-An Internal-external Constrained Distillation Framework for Continual Semantic Segmentation.-MTD: Multi-Timestep Detector for Delayed Streaming Perception.-Semi-Direct SLAM with Manhattan for Indoor Low-texture Environment.-L2T-BEV: Local Lane Topology Prediction from Onboard Surround-View Cameras in Bird s Eye View Perspective.-CCLane: Concise Curve Anchor-based Lane Detection Model with MLP-Mixer.-Uncertainty-aware Boundary Attention Network for Real-time Semantic Segmentation.-URFormer: Unified Representation LiDAR-Camera 3D Object Detection with Transformer.-A Single-Stage 3D Object Detection Method Based on Sparse Attention Mechanism.-WaRoNav: Warehouse Robot Navigation Based on Multi-View Visual-Inertial Fusion.-Enhancing Lidar and Radar Fusion for Vehicle Detection in Adverse Weather via Cross-Modality Semantic Consistency.-Enhancing Active Visual Tracking under Distractor Environments.-Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation.-Cross-task Physical Adversarial Attack against Lane Detection System Based on LED Illumination Modulation.-RECO: Rotation Equivariant COnvolutional Neural Network for Human Trajectory Forecasting.-FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13-15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xiv, 521 Seiten Illustrationen, Diagramme |
| ISBN: | 9789819984343 978-981-99-8434-3 |