Pattern recognition and computer vision Part 9
Decoupled Contrastive Learning for Long-Tailed Distribution.-MFNet: A Channel Segmentation-based Hierarchical Network for Multi-Food Recognition.-Improving the Adversarial Robustness of Object Detection with Contrastive Learning.-CAWNet: A Channel Attention Watermarking Attack Network Based on CWABl...
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| Weitere Verfasser: | , , , , , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Singapore
Springer
2024
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| Schriftenreihe: | Lecture notes in computer science
14433 |
| Schlagworte: |
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| Zusammenfassung: | Decoupled Contrastive Learning for Long-Tailed Distribution.-MFNet: A Channel Segmentation-based Hierarchical Network for Multi-Food Recognition.-Improving the Adversarial Robustness of Object Detection with Contrastive Learning.-CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock.-Global Consistency Enhancement Network for Weakly-Supervised Semantic Segmentation.-Enhancing Model Robustness against Adversarial Attacks with an Anti-Adversarial Module.-FGPTQ-ViT:Fine-grained Post-training Quantization for Vision Transformers.-Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting.-DeCAB: Debiased Semi-Supervised Learning for Imbalanced Open-Set Data.-An Effective Visible-Infrared Person Re-identification Network based on Second-Order Attention and Mixed Intermediate Modality.-Quadratic polynomial residual network for no-reference image quality assessment.-Interactive Learning for Interpretable Visual Recognition via Semantic-Aware Self-Teaching Framework.-Adaptive and Compact Graph Convolutional Network for Micro-Expression Recognition.-Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI.-A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction.-A Representation Learning Link Prediction Approach Using Line Graph Neural Networks.-Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-based Data.-Federated Learning Based on Diffusion Model to Cope with non-IID DataSFRSwin: A shallow significant feature retention Swin Transformer for fine-grained image classification of wildlife species.-A robust and high accurate method for hand kinematics decoding from neural populations.-Multi-head Attention Induced Dynamic Hypergraph Convolutional Networks.-Self Supervised Temporal Ultrasound Reconstruction for Muscle Atrophy EvaluationSalient Object Detection Using Reciprocal Learning.-Graphormer-based Contextual Reasoning Network for Small Object Detection.-PVT-Crowd:Bridging Multi-scale Features from Pyramid Vision Transformer for Weakly-Supervised Crowd Counting.-Multi-view Contrastive Learning Network for Recommendation.-Uncertainty-confidence fused pseudo-labeling for Graph Neural Networks.-FSCD-Net: A Few-Shot Stego Cross-Domain Net for Image Steganalysis.-Preference Contrastive Learning for Personalized Recommendation.-GLViG: Global and Local Vision GNN May be What You Need for Vision.-SVDML: Semantic and Visual space Deep Mutual Learning for Zero-Shot Learning.-Heterogeneous Graph Attribute Completion via Efficient Meta-path Context-aware Learning.-Fine-Grain Classification Method of Non-Small Cell Lung Cancer Based on Progressive Jigsaw and Graph Convolutional Network.-Improving Transferability of Adversarial Attacks with Gaussian Gradient Enhance Momentum.-Boundary Guided Feature fusion Network for Camouflaged Object Detection.-Saliency Driven Monocular Depth Estimation based on Multi-scale Graph Convolutional Network.-Mask-guided Joint Single Image Specular Highlight Detection and Removal.-CATrack: Convolution and Attention Feature Fusion for Visual Object TrackingSText-DETR: End-to-End Arbitrary-Shaped Text Detection with Scalable Query in Transformer.-SSHRF-GAN: Spatial-Spectral Joint High Receptive Field GAN for Old Photo Restoration 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, 507 Seiten Illustrationen, Diagramme |
| ISBN: | 9789819985456 978-981-99-8545-6 |