Pattern Recognition and Computer Vision
Springer Nature (Verlag)
978-981-97-8489-9 (ISBN)
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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.
Auto-USOD: Searching Topology for Underwater Salient Object Detection.- MBA-NER: Multi-Granularity Entity Boundary-Aware Contrastive Enhanced for Two-stage Few-Shot Named Entity Recognition.- Meta-Learning Based Knowledge Distillation for Domain Adaptive Nighttime Segmentation.- Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts.- Towards Adversarial-Robust Class-Incremental Learning via Progressively Volume-up Perturbation Generation.- Neighborhood Difference-Enhanced Graph Neural Network based on Hypergraph for Social Bot Detection.- SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations.- An Entropy-based Pseudo-Label Mixup Method for Source-Free Domain Adaptation.- DAMS: Document Image Steganography with Dual Attention Multi-Scale Encoder-Decoder Architecture.- Dual-Task Cascaded for Proactive Deepfake Detection Using QPCET watermarking.- XrGroup: Graph Convolutional Networks for Group-Aware Pedestrian Trajectory Prediction with Speed information.- Generative Steganography Based on Dual-Branch Flow.- Invisible Backdoor Attack Through Singular Value Decomposition.- Self-supervised transformer-based pre-training method with General Plant Infection dataset.- Spatio-Temporal Perceiving Network Based Vision Transformer for 6-Hour Precipitation Prediction Using Multi-Meteorological Factors.- Learning Local Spatial and Global Context Activation for Visual Recognition.- A Novel Method for Autism Identification based on Multi-Atlas Features Fusion and Graph Neural Network.- CRFNet: A medical image segmentation method using the cross attention mechanism and refined feature fusion strategy.- SCC-CAM: Weakly Supervised Segmentation on Brain Tumor MRI with Similarity Constraint and Causality.- Global Structural Consistency Set Transformer.- IMO-Net: Integrated Memory Optimization Network for Video Instance Lane Detection.- Lightweight Facial Expression Recognition Based on Hybrid Multiscale and Multi-Head Collaborative Attention.- Single model learns multiple styles of Chinese calligraphy via Style Collection Mechanism.- FusionNet for Interactive Image Segmentation.- Dynamic Spatial-Temporal Perception Graph Convolutional Networks for Traffic Flow Forecasting.- Foreign object classification for coal conveyor belts based on deep learning.- Reducing Memory Footprint in Deep Network Training by Gradient Space Reutilization.- Interpretable Unsupervised Homography Estimation.- DRC-NET: Density Reweighted Convolution Network for Edge Curve Extraction.- Unsupervised Underwater Image Enhancement Combining Imaging Restoration and Prompt Learning.- Generatice Adversarial Imitation Learning Algorithm based on Improved Curiosity Module.- Zero-shot Blind Face Restoration via Conditional Diffusion Sampling.- Task-aware Few-shot Image Generation via Dynamic Local Distribution Estimation and Sampling.- Adversarial Training and Contrastive Learning with Bidirectional Transformers for Sequence Recommendation.- Empathizing Before Generation: A Double-layered Framework for Emotional Support LLM.
| Erscheint lt. Verlag | 18.12.2024 |
|---|---|
| Reihe/Serie | Lecture Notes in Computer Science |
| Zusatzinfo | X, 494 p. |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 981-97-8489-1 / 9819784891 |
| ISBN-13 | 978-981-97-8489-9 / 9789819784899 |
| Zustand | Neuware |
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