Pattern Recognition and Computer Vision
portes grátis
Pattern Recognition and Computer Vision
7th Chinese Conference, PRCV 2024, Urumqi, China, October 18-20, 2024, Proceedings, Part XV
He, Ran; Ubul, Kurban; Silamu, Wushouer; Cheng, Ming-Ming; Zhou, Jie; Zha, Hongbin; Lin, Zhouchen; Liu, Cheng-Lin
Springer Verlag, Singapore
11/2024
561
Mole
9789819784981
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Anchored Supervised Contrastive Learning for Long-Tailed Medical Image Regression.- Dynamic Feature Fusion Based on Consistency and Complementarity of Brain Atlases.- FUF-TransUNet: a transformer-based U-Net with fully utilize of features for liver and liver-tumor segmentation in CT images.- Dual-View Dual-Boundary Dual U-Nets for Multiscale Segmentation of Oral CBCT ImagesA Novel Diffusion Model with Wavelet Transform for Optic Disc and Cup Segmentation in Fundus Images.- STCTb: A Spatio-Temporal Collaborative Transformer Block for Brain Diseases Classification using fMRI Time Series.A Generalized Contrast-adjustment Guided Growth Method for Medical Image Segmentation.- MDNet: Morphology-Driven Weakly Supervised Polyp DetectionMMR-Sleep: A Multi-Channel and Multi-Receptive Field Sleep Stage recognition Model.- CPNet: Cross Prototype Network for Few-shot Medical Image Segmentation.- SBC-UNet: A Network Based on Improved Hourglass Attention Mechanism and U-Net for Medical Image Segmentation.- Bridge the gap of semantic context: A Boundary-guided Context Fusion UNet for Medical Image Segmentation.- Bilinear Fine-grained Classification of Ultrasound Images Integrated with Interpretable Radiomics.- GCNet: Global context-guided uncertainty boundary for polyp segmentation.- Comprehensive Transformer Integration Network (CTIN): Advancing Endoscopic Disease Segmentation with Hybrid Transformer Architecture.- IPM: An Intelligent Component for 3D Brain Tumor Segmentation Integrating Semantic Extractor and Pixel RefinerEdge-Net: A Self-supervised Medical Image Segmentation Model Based on Edge Attention.- Fundus image disease diagnosis and quality assessment based on dual-task collaborative optimization.- Multi-modality Correlation Learning Network for Pediatric Ventricular Septal Defects Identification.- MFIS-net: A Deep Learning Framework for Left Atrial Segmentation.- Semi-Supervised Gland Segmentation via Label Purification and Reliable Pixel Learning.- DFANet: A Dual-stream Deep Feature Aware Network for Multi-focus Image FusionMST-Gait?Application of Multi-Scale Temporal Modeling to Gait Recognition.- Identity-Preserving Animal Image Generation for Animal Individual Identification.- FIL-FLD: Few-shot Incremental Learning with EMD Metric for High Generalization Fingerprint Liveness DetectionText Based Unsupervised Domain Generalization Person Re-identificationSF-Gait: Two-Stage Temporal Compression Network for Learning Gait Micro-Motions and Cycle Patterns.- Coarse-to-Fine Domain Adaptation for Cross-subject EEG Emotion Recognition with Contrastive Learning.- Face Anti-spoofing based on Multi-view Anomaly Detection. -Online Signature Verification Based on Recurrent Attentional Time-Delay Neural Networks.- Multimodal finger recognition based on feature fusion attention for fingerprints, finger-veins, and f inger-knuckle-prints. -Hierarchical Discrepancy-aware Interaction Network for Face Forgery DetectionAU-vMAE: Knowledge-Guide Action Units Detection via Video Masked AutoencoderTransformer-based Multimodal Spatial-Temporal Fusion for Gait Recognition.- Multi-level Distributional Discrepancy Enhancement for Cross Domain Face Forgery Detection.- Unsupervised person Re-ID based on nonlinear asymmetric metric learning.- FR-watermarking: A Fusion Framework for Face-Based Digital Watermarking.- Enhancing Semi-Dense Feature Matching through Probabilistic Modeling of Cascaded Supervision and Consistency.- Concentrating Estimation Attention: Human Prior Constrained Methods for Robust Classification.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Multi-modal learning;image processing;machine learning;object detection;object recognition;object tracking;pattern recognition;signal processing;remote sensing;action recognition;deep learning;neural network;feature extraction;computer vision;3D vision;video understanding;character recognition;document analysis;biometric recognition
Anchored Supervised Contrastive Learning for Long-Tailed Medical Image Regression.- Dynamic Feature Fusion Based on Consistency and Complementarity of Brain Atlases.- FUF-TransUNet: a transformer-based U-Net with fully utilize of features for liver and liver-tumor segmentation in CT images.- Dual-View Dual-Boundary Dual U-Nets for Multiscale Segmentation of Oral CBCT ImagesA Novel Diffusion Model with Wavelet Transform for Optic Disc and Cup Segmentation in Fundus Images.- STCTb: A Spatio-Temporal Collaborative Transformer Block for Brain Diseases Classification using fMRI Time Series.A Generalized Contrast-adjustment Guided Growth Method for Medical Image Segmentation.- MDNet: Morphology-Driven Weakly Supervised Polyp DetectionMMR-Sleep: A Multi-Channel and Multi-Receptive Field Sleep Stage recognition Model.- CPNet: Cross Prototype Network for Few-shot Medical Image Segmentation.- SBC-UNet: A Network Based on Improved Hourglass Attention Mechanism and U-Net for Medical Image Segmentation.- Bridge the gap of semantic context: A Boundary-guided Context Fusion UNet for Medical Image Segmentation.- Bilinear Fine-grained Classification of Ultrasound Images Integrated with Interpretable Radiomics.- GCNet: Global context-guided uncertainty boundary for polyp segmentation.- Comprehensive Transformer Integration Network (CTIN): Advancing Endoscopic Disease Segmentation with Hybrid Transformer Architecture.- IPM: An Intelligent Component for 3D Brain Tumor Segmentation Integrating Semantic Extractor and Pixel RefinerEdge-Net: A Self-supervised Medical Image Segmentation Model Based on Edge Attention.- Fundus image disease diagnosis and quality assessment based on dual-task collaborative optimization.- Multi-modality Correlation Learning Network for Pediatric Ventricular Septal Defects Identification.- MFIS-net: A Deep Learning Framework for Left Atrial Segmentation.- Semi-Supervised Gland Segmentation via Label Purification and Reliable Pixel Learning.- DFANet: A Dual-stream Deep Feature Aware Network for Multi-focus Image FusionMST-Gait?Application of Multi-Scale Temporal Modeling to Gait Recognition.- Identity-Preserving Animal Image Generation for Animal Individual Identification.- FIL-FLD: Few-shot Incremental Learning with EMD Metric for High Generalization Fingerprint Liveness DetectionText Based Unsupervised Domain Generalization Person Re-identificationSF-Gait: Two-Stage Temporal Compression Network for Learning Gait Micro-Motions and Cycle Patterns.- Coarse-to-Fine Domain Adaptation for Cross-subject EEG Emotion Recognition with Contrastive Learning.- Face Anti-spoofing based on Multi-view Anomaly Detection. -Online Signature Verification Based on Recurrent Attentional Time-Delay Neural Networks.- Multimodal finger recognition based on feature fusion attention for fingerprints, finger-veins, and f inger-knuckle-prints. -Hierarchical Discrepancy-aware Interaction Network for Face Forgery DetectionAU-vMAE: Knowledge-Guide Action Units Detection via Video Masked AutoencoderTransformer-based Multimodal Spatial-Temporal Fusion for Gait Recognition.- Multi-level Distributional Discrepancy Enhancement for Cross Domain Face Forgery Detection.- Unsupervised person Re-ID based on nonlinear asymmetric metric learning.- FR-watermarking: A Fusion Framework for Face-Based Digital Watermarking.- Enhancing Semi-Dense Feature Matching through Probabilistic Modeling of Cascaded Supervision and Consistency.- Concentrating Estimation Attention: Human Prior Constrained Methods for Robust Classification.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Multi-modal learning;image processing;machine learning;object detection;object recognition;object tracking;pattern recognition;signal processing;remote sensing;action recognition;deep learning;neural network;feature extraction;computer vision;3D vision;video understanding;character recognition;document analysis;biometric recognition
