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DISPONIBILITÀ IMMEDIATA
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Libro
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Pattern Recognition and Computer Vision
liu qingshan (curatore); wang hanzi (curatore); ma zhanyu (curatore); zheng weishi (curatore); zha hongbin (curatore); chen xilin (curatore); wang liang (curatore); ji rongrong (curatore)
80,98 €
76,93 €
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TRAMA
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.SOMMARIO
Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification.- Multi-scale Dilated Attention Graph Convolutional Network for Skeleton-Based Action Recognition.- Auto-Learning-GCN: An Ingenious Framework for Skeleton-based Action Recognition.- Skeleton-based Action Recognition with Combined Part-wise Topology Graph Convolutional Networks.- Segmenting Key Clues to Induce Human-Object Interaction Detection.- Lightweight Multispectral Skeleton and Multi-Stream Graph Attention Networks for Enhanced Action Prediction with Multiple Modalities.- Spatio-temporal Self-supervision for Few-shot Action Recognition.- A Fuzzy Error based Fine-tune Method for Spatio-temporal Recognition Model.- Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition.- HFGCN-Based Action Recognition System for Figure Skating.- Image Priors Assisted Pre-training for Point Cloud Shape Analysis.-AMM-GAN: Attribute-Matching Memory for Person Text-to-Image Generation.- RecFormer: Recurrent Multi-modal Transformer with History-aware Contrastive Learning for Visual Dialog.- KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing.- Enhancing Text-Image Person Retrieval through Nuances Varied Sample.- Unsupervised Prototype Adapter for Vision-Language Models.- Multimodal Causal Relations Enhanced CLIP for Image-to-Text Retrieval.- Exploring Cross-Modal Inconsistency in Entities and Emotions for Multimodal Fake News Detection.- Deep Consistency Preserving Network for Unsupervised Cross-modal Hashing.- Learning Adapters for Text-guided Portrait Stylization with Pretrained Diffusion Models.- EdgeFusion: Infrared and Visible Image Fusion Algorithm in Low Light.- An Efficient Momentum Framework for Face-Voice Association Learning.- Multi-modal Instance Refinement for Cross-domain Action Recognition.- Modality Interference Decoupling and Representation Alignment for Caricature-Visual Face Recognition.- Plugging Stylized Controls in Open-Stylized Image Captioning.- MGT: Modality-Guided Transformer for Infrared and Visible Image Fusion.- Multimodal Rumor Detection by Using Additive Angular Margin with Class-aware Attention for Hard Samples.- An Effective Dynamic Reweighting Method for Unbiased Scene Graph Generation.- Multi-modal Graph and Sequence Fusion Learning for Recommendation.- Co-attention guided local-global feature fusion for aspect-level multimodal sentiment analysis.- Discovering Multimodal Hierarchical Structures with Graph Neural Networks for Multi-modal and Multi-hop Question Answering.- Enhancing Recommender System with Multi-modal Knowledge Graph.- Location Attention Knowledge Embedding Model for Image-Text Matching.- Pedestrian Attribute Recognition Based on Multimodal Transformer.- RGB-D Road Segmentation Based on Geometric Prior Information.- Contrastive Perturbation Network for Weakly Supervised Temporal Sentence Grounding.- MLDF-Net: Metadata Based Multi-level Dynamic Fusion Network.- Efficient Adversarial Training with Membership Inference Resistance.- Enhancing Image Comprehension for Computer Science Visual Question Answering.- Cross-Modal Attentive Recalibration and Dynamic Fusion for Multispectral Pedestrian Detection.ALTRE INFORMAZIONI
- Condizione: Nuovo
- ISBN: 9789819984282
- Collana: Lecture Notes in Computer Science
- Dimensioni: 235 x 155 mm Ø 801 gr
- Formato: Brossura
- Illustration Notes: XIV, 513 p. 159 illus., 152 illus. in color.
- Pagine Arabe: 513
- Pagine Romane: xiv