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Learning-based local visual representation and indexing / / by Rongrong Ji [and four others]
| Learning-based local visual representation and indexing / / by Rongrong Ji [and four others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam, [Netherlands] : , : Elsevier, , 2015 |
| Descrizione fisica | 1 online resource (128 p.) |
| Disciplina | 006.37 |
| Soggetto topico |
Computer vision
Pattern recognition systems |
| ISBN | 0-12-802409-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Front Cover""; ""Learning-Based Local Visual Representation and Indexing""; ""Copyright""; ""Contents""; ""Preface""; ""List of Figures""; ""List of Tables""; ""List of Algorithms""; ""Chapter 1: Introduction""; ""1.1 Background and Significance""; ""1.2 Literature Review of the Visual Dictionary""; ""1.2.1 Local Interest-Point Extraction""; ""1.2.2 Visual-Dictionary Generation and Indexing ""; ""1.3 Contents of This Book""; ""Chapter 2: Interest-Point Detection: Beyond Local Scale""; ""2.1 Introduction""; ""2.2 Difference of Contextual Gaussians""; ""2.2.1 Local Interest-Point Detection""
""2.2.2 Accumulating Contextual Gaussian Difference """"2.3 Mean Shift-Based Localization""; ""2.3.1 Localization Algorithm ""; ""2.3.2 Comparison to Saliency""; ""2.4 Detector Learning""; ""2.5 Experiments""; ""2.5.1 Database and Evaluation Criteria""; ""2.5.2 Detector Repeatability""; ""2.5.3 CASL for Image Search and Classification""; ""2.6 Summary""; ""Chapter 3: Unsupervised Dictionary Optimization""; ""3.1 Introduction""; ""3.2 Density-Based Metric Learning""; ""3.2.1 Feature-Space Density-Field Estimation ""; ""3.2.2 Learning a Metric for Quantization"" ""3.3 Chain-Structure Recognition """"3.3.1 Chain Recognition in Dictionary Hierarchy""; ""3.4 Dictionary Transfer Learning""; ""3.4.1 Cross-database Case""; ""3.4.2 Incremental Transfer""; ""3.5 Experiments""; ""3.5.1 Quantitative results""; ""3.6 Summary""; ""Chapter 4: Supervised Dictionary Learning via Semantic Embedding ""; ""4.1 Introduction""; ""4.2 Semantic Labeling Propagation""; ""4.2.1 Density Diversity Estimation ""; ""4.3 Supervised Dictionary Learning""; ""4.3.1 Generative Modeling ""; ""4.3.2 Supervised Quantization ""; ""4.4 Experiments"" ""4.4.1 Database and Evaluations""""4.4.2 Quantitative Results""; ""4.5 Summary""; ""Chapter 5: Visual Pattern Mining""; ""5.1 Introduction""; ""5.2 Discriminative 3D Pattern Mining""; ""5.2.1 The Proposed Mining Scheme""; ""5.2.2 Sparse Pattern Coding""; ""5.3 CBoP for Low Bit Rate Mobile Visual Search""; ""5.4 Quantitative Results""; ""5.4.1 Data Collection""; ""5.4.2 Evaluation Criteria""; ""5.4.3 Baselines""; ""5.4.4 Quantitative Performance""; ""5.5 Conclusion""; ""Conclusions""; ""References"" |
| Record Nr. | UNINA-9910797028803321 |
| Amsterdam, [Netherlands] : , : Elsevier, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learning-based local visual representation and indexing / / by Rongrong Ji [and four others]
| Learning-based local visual representation and indexing / / by Rongrong Ji [and four others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam, [Netherlands] : , : Elsevier, , 2015 |
| Descrizione fisica | 1 online resource (128 p.) |
| Disciplina | 006.37 |
| Soggetto topico |
Computer vision
Pattern recognition systems |
| ISBN | 0-12-802409-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Front Cover""; ""Learning-Based Local Visual Representation and Indexing""; ""Copyright""; ""Contents""; ""Preface""; ""List of Figures""; ""List of Tables""; ""List of Algorithms""; ""Chapter 1: Introduction""; ""1.1 Background and Significance""; ""1.2 Literature Review of the Visual Dictionary""; ""1.2.1 Local Interest-Point Extraction""; ""1.2.2 Visual-Dictionary Generation and Indexing ""; ""1.3 Contents of This Book""; ""Chapter 2: Interest-Point Detection: Beyond Local Scale""; ""2.1 Introduction""; ""2.2 Difference of Contextual Gaussians""; ""2.2.1 Local Interest-Point Detection""
""2.2.2 Accumulating Contextual Gaussian Difference """"2.3 Mean Shift-Based Localization""; ""2.3.1 Localization Algorithm ""; ""2.3.2 Comparison to Saliency""; ""2.4 Detector Learning""; ""2.5 Experiments""; ""2.5.1 Database and Evaluation Criteria""; ""2.5.2 Detector Repeatability""; ""2.5.3 CASL for Image Search and Classification""; ""2.6 Summary""; ""Chapter 3: Unsupervised Dictionary Optimization""; ""3.1 Introduction""; ""3.2 Density-Based Metric Learning""; ""3.2.1 Feature-Space Density-Field Estimation ""; ""3.2.2 Learning a Metric for Quantization"" ""3.3 Chain-Structure Recognition """"3.3.1 Chain Recognition in Dictionary Hierarchy""; ""3.4 Dictionary Transfer Learning""; ""3.4.1 Cross-database Case""; ""3.4.2 Incremental Transfer""; ""3.5 Experiments""; ""3.5.1 Quantitative results""; ""3.6 Summary""; ""Chapter 4: Supervised Dictionary Learning via Semantic Embedding ""; ""4.1 Introduction""; ""4.2 Semantic Labeling Propagation""; ""4.2.1 Density Diversity Estimation ""; ""4.3 Supervised Dictionary Learning""; ""4.3.1 Generative Modeling ""; ""4.3.2 Supervised Quantization ""; ""4.4 Experiments"" ""4.4.1 Database and Evaluations""""4.4.2 Quantitative Results""; ""4.5 Summary""; ""Chapter 5: Visual Pattern Mining""; ""5.1 Introduction""; ""5.2 Discriminative 3D Pattern Mining""; ""5.2.1 The Proposed Mining Scheme""; ""5.2.2 Sparse Pattern Coding""; ""5.3 CBoP for Low Bit Rate Mobile Visual Search""; ""5.4 Quantitative Results""; ""5.4.1 Data Collection""; ""5.4.2 Evaluation Criteria""; ""5.4.3 Baselines""; ""5.4.4 Quantitative Performance""; ""5.5 Conclusion""; ""Conclusions""; ""References"" |
| Record Nr. | UNINA-9910813129403321 |
| Amsterdam, [Netherlands] : , : Elsevier, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part X / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part X / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (509 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer networks Computer systems Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9985-49-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part X -- Neural Network and Deep Learning III -- Dual-Stream Context-Aware Neural Network for Survival Prediction from Whole Slide Images -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 4 Conclusion -- References -- A Multi-label Image Recognition Algorithm Based on Spatial and Semantic Correlation Interaction -- 1 Introduction -- 2 Related Work -- 2.1 Correlation-Agnostic Algorithms -- 2.2 Spatial Correlation Algorithms -- 2.3 Semantic Correlation Algorithms -- 3 Methodology -- 3.1 Definition of Multi-label Image Recognition -- 3.2 The Framework of SSCI -- 3.3 Loss Function -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with Other Mainstream Algorithms -- 4.4 Evaluation of the SSCI Effectiveness -- 5 Conclusion -- References -- Hierarchical Spatial-Temporal Network for Skeleton-Based Temporal Action Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Temporal Action Segmentation -- 2.2 Skeleton-Based Action Recognition -- 3 Method -- 3.1 Network Architecture -- 3.2 Multi-Branch Transfer Fusion Module -- 3.3 Multi-Scale Temporal Convolution Module -- 3.4 Loss Function -- 4 Experiments -- 4.1 Setup -- 4.2 Effect of Hierarchical Model -- 4.3 Effect of Multiple Modalties -- 4.4 Effect of Multi-modal Fusion Methods -- 4.5 Effect of Multi-Scale Temporal Convolution -- 4.6 Comparision with State-of-the-Art -- 5 Conclusion -- References -- Multi-behavior Enhanced Graph Neural Networks for Social Recommendation -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methodology -- 4.1 Embedding Layer -- 4.2 Propagation Layer -- 4.3 Multi-behavior Integration Layer -- 4.4 Prediction Layer -- 4.5 Model Training -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Performance Comparison (RQ1) -- 5.3 Ablation Study (RQ2).
5.4 Parameter Analysis (RQ3) -- 6 Conclusion and Future Work -- References -- A Complex-Valued Neural Network Based Robust Image Compression -- 1 Introduction -- 2 Related Works -- 2.1 Neural Image Compression -- 2.2 Adversarial Attack -- 2.3 Complex-Valued Convolutional Neural Networks -- 3 Proposed Method -- 3.1 Overall Framework -- 3.2 Nonlinear Transform -- 4 Experiment Results -- 4.1 Experiment Setup -- 4.2 Results and Comparison -- 4.3 Ablation Study -- 5 Conclusions -- References -- Binarizing Super-Resolution Neural Network Without Batch Normalization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Batch Normalization in SR Models -- 3.2 Channel-Wise Asymmetric Binarizer for Activations -- 3.3 Smoothness-Controlled Estimator -- 4 Experimentation -- 4.1 Experiment Setup -- 4.2 Ablation Study -- 4.3 Visualization -- 5 Conclusion -- References -- Infrared and Visible Image Fusion via Test-Time Training -- 1 Introduction -- 2 Method -- 2.1 Overall Framework -- 2.2 Training and Testing -- 3 Experiments -- 3.1 Experiment Configuration -- 3.2 Performance Comparison on TNO -- 3.3 Performance Comparison on VIFB -- 3.4 Ablation Study -- 4 Conclusion -- References -- Graph-Based Dependency-Aware Non-Intrusive Load Monitoring -- 1 Introduction -- 2 Proposed Method -- 2.1 Problem Formulation -- 2.2 Co-occurrence Probability Graph -- 2.3 Graph Structure Learning -- 2.4 Graph Attention Neural Network -- 2.5 Encoder-Decoder Module -- 3 Numerical Studies and Discussions -- 3.1 Dataset and Experiment Setup -- 3.2 Metrics and Comparisons -- 4 Conclusion -- References -- Few-Shot Object Detection via Classify-Free RPN -- 1 Introduction -- 2 Related Work -- 2.1 Object Detection -- 2.2 Few-Shot Learning -- 2.3 Few-Shot Object Detection -- 3 Methodology -- 3.1 Problem Setting -- 3.2 Analysis of the Base Class Bias Issue in RPN -- 3.3 Classify-Free RPN. 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison with the State-of-the-Art -- 4.3 Ablation Study -- 5 Conclusion -- References -- IPFR: Identity-Preserving Face Reenactment with Enhanced Domain Adversarial Training and Multi-level Identity Priors -- 1 Introduction -- 2 Methods -- 2.1 Target Motion Encoder and 3D Shape Encoder -- 2.2 3D Shape-Aware Warping Module -- 2.3 Identity-Aware Refining Module -- 2.4 Enhanced Domain Discriminator -- 2.5 Training -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Comparisons -- 3.3 Ablation Study -- 4 Limitation -- 5 Conclusion -- References -- L2MNet: Enhancing Continual Semantic Segmentation with Mask Matching -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries and Revisiting -- 3.2 Proposed Learn-to-Match Framework -- 3.3 Training Loss -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Quantitative Evaluation -- 4.3 Ablation Study -- 5 Conclusion -- References -- Adaptive Channel Pruning for Trainability Protection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Method Framework and Motivation -- 3.2 Channel Similarity Calculation and Trainability Preservation -- 3.3 Sparse Control and Optimization -- 4 Experiments -- 4.1 Experiments Settings and Evaluation Metrics -- 4.2 Results on Imagenet -- 4.3 Results on Cifar-10 -- 4.4 Results on YOLOX-s -- 4.5 Ablation -- 5 Conclusion -- References -- Exploiting Adaptive Crop and Deformable Convolution for Road Damage Detection -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Adaptive Image Cropping Based on Vanishing Point Estimation -- 3.2 Feature Learning with Deformable Convolution -- 3.3 Diagonal Intersection over Union Loss Function -- 4 Experiment -- 4.1 Comparative Analysis of Different Datasets -- 4.2 Ablation Analysis -- 5 Conclusion -- References -- Cascaded-Scoring Tracklet Matching for Multi-object Tracking. 1 Introduction -- 2 Related Work -- 2.1 Tracking by Detection -- 2.2 Joint Detection and Tracking -- 3 Proposed Method -- 3.1 Cascaded-Scoring Tracklet Matching -- 3.2 Motion-Guided Based Target Aware -- 3.3 Appearance-Assisted Feature Warper -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Boosting Generalization Performance in Person Re-identification -- 1 Introduction -- 2 Related Work -- 2.1 Generalizable Person ReID -- 2.2 Vision-Language Learning -- 3 Method -- 3.1 Review of CLIP -- 3.2 A Novel Cross-Modal Framework -- 3.3 Prompt Design Process -- 3.4 Loss Function -- 4 Experiments -- 4.1 Datasets and Evaluation Protocols -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with State-of-the-Art Methods -- 4.5 Other Analysis -- 5 Conclusion -- References -- Self-guided Transformer for Video Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Video Super-Resolution -- 2.2 Vision Transformers -- 3 Our Method -- 3.1 Network Overview -- 3.2 Multi-headed Self-attention Module Based on Offset-Guided Window (OGW-MSA) -- 3.3 Feature Aggregation (FA) -- 4 Experiments -- 4.1 Datasets and Experimental Settings -- 4.2 Comparisons with State-of-the-Art Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- SAMP: Sub-task Aware Model Pruning with Layer-Wise Channel Balancing for Person Search -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Framework Overview -- 3.2 Sub-task Aware Channel Importance Estimation -- 3.3 Layer-Wise Channel Balancing -- 3.4 Adaptive OIM Loss for Model Pruning and Finetuning -- 4 Experimental Results and Analysis -- 4.1 Dataset and Evaluation Metric -- 4.2 Implementation Details -- 4.3 Comparison with the State-of-the-Art Approaches -- 4.4 Ablation Study -- 5 Conclusion. References -- MKB: Multi-Kernel Bures Metric for Nighttime Aerial Tracking -- 1 Introduction -- 2 Methodology -- 2.1 Kernel Bures Metric -- 2.2 Multi-Kernel Bures Metric -- 2.3 Objective Loss -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Evaluation Datasets -- 3.3 Comparison Results -- 3.4 Visualization -- 3.5 Ablation Study -- 4 Conclusion -- References -- Deep Arbitrary-Scale Unfolding Network for Color-Guided Depth Map Super-Resolution -- 1 Introduction -- 2 The Proposed Method -- 2.1 Problem Formulation -- 2.2 Algorithm Unfolding -- 2.3 Continuous Up-Sampling Fusion (CUSF) -- 2.4 Loss Function -- 3 Experimental Results -- 3.1 Implementation Details -- 3.2 The Quality Comparison of Different DSR Methods -- 3.3 Ablation Study -- 4 Conclusion -- References -- SSDD-Net: A Lightweight and Efficient Deep Learning Model for Steel Surface Defect Detection -- 1 Introduction -- 2 Methods -- 2.1 LMFE: Light Multiscale Feature Extraction Module -- 2.2 SEFF: Simple Effective Feature Fusion Network -- 2.3 SSDD-Net -- 3 Experiments and Analysis -- 3.1 Implementation Details -- 3.2 Evaluation Metrics -- 3.3 Dataset -- 3.4 Ablation Studies -- 3.5 Comparison with Other SOTA Methods -- 3.6 Comprehensive Performance of SSDD-Net -- 4 Conclusion -- References -- Effective Small Ship Detection with Enhanced-YOLOv7 -- 1 Introduction -- 2 Method -- 2.1 Small Object-Aware Feature Extraction Module (SOAFE) -- 2.2 Small Object-Friendly Scale-Insensitive Regression Scheme (SOFSIR) -- 2.3 Geometric Constraint-Based Non-Maximum Suppression Method (GCNMS) -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Quantitative Analysis -- 3.3 Ablation Studies -- 3.4 Qualitative Analysis -- 4 Conclusion -- References -- PiDiNeXt: An Efficient Edge Detector Based on Parallel Pixel Difference Networks -- 1 Introduction -- 2 Related Work. 2.1 The Development of Deep Learning Based Edge Detection. |
| Record Nr. | UNINA-9910799223503321 |
Liu Qingshan
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (514 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer networks Computer systems Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9984-62-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pattern Classification and Cluster Analysis -- Performance Evaluation and Benchmarks -- Remote Sensing Image Interpretation. |
| Record Nr. | UNINA-9910799217603321 |
|
Liu Qingshan
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part V / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part V / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (542 pages) |
| Disciplina |
621.39
004.6 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer engineering
Computer networks Image processing - Digital techniques Computer vision Computer systems Machine learning Computer Engineering and Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9984-69-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Biometric Recognition -- Face Recognition and Pose Recognition -- Structural Pattern Recognition. |
| Record Nr. | UNINA-9910799207103321 |
|
Liu Qingshan
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VI / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VI / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (534 pages) |
| Disciplina |
621.39
004.6 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computer engineering
Computer networks Image processing - Digital techniques Computer vision Computer systems Machine learning Computer Engineering and Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9985-37-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Computational Photography, Sensing and Display Technology -- Video Analysis and Understanding -- Vision Applications and Systems. |
| Record Nr. | UNINA-9910799212003321 |
|
Liu Qingshan
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part XIII / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part XIII / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (524 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer networks Computer systems Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9985-58-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part XIII -- Medical Image Processing and Analysis -- Growth Simulation Network for Polyp Segmentation -- 1 Introduction -- 2 The Proposed Method -- 2.1 Gaussian Map and Body Map -- 2.2 Overall Architecture -- 2.3 Features Extraction and Fusion Module -- 2.4 Dynamic Attention Guidance Module -- 2.5 Dynamic Simulation Loss -- 3 Experiments -- 3.1 Settings -- 3.2 Comparisons with State-of-the-art -- 3.3 Ablation Study -- 4 Conclusion -- References -- Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Feature Extraction Module -- 3.2 Brain Diffuser -- 3.3 GCN Classifier -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset and Preprocessing -- 4.2 Experiment Configuration -- 4.3 Results and Discussion -- 5 Conclusion -- References -- CCJ-SLC: A Skin Lesion Image Classification Method Based on Contrastive Clustering and Jigsaw Puzzle -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview of Our Method -- 3.2 Contrastive Clustering -- 3.3 Jigsaw Puzzle -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Baseline Performance -- 4.3 Ablation Experiment -- 4.4 Analysis -- 5 Conclusion -- References -- A Real-Time Network for Fast Breast Lesion Detection in Ultrasound Videos -- 1 Introduction -- 2 Method -- 2.1 Space Time Feature Aggregation (STA) Module -- 3 Experiments and Results -- 3.1 Comparisons with State-of-the-Arts -- 3.2 Ablation Study -- 3.3 Generalizability of Our Network -- 4 Conclusion -- References -- CBAV-Loss: Crossover and Branch Losses for Artery-Vein Segmentation in OCTA Images -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Crossover Loss and Branch Loss -- 2.3 Loss Function -- 3 Experiments -- 3.1 Data -- 3.2 Experimental Settings -- 3.3 Evaluation Metrics.
3.4 Ablation Study on CBAV-Loss -- 3.5 Influence of the Proposed Loss on Different Segmentation Networks -- 4 Conclusion -- References -- Leveraging Data Correlations for Skin Lesion Classification -- 1 Introduction -- 2 Related Work -- 2.1 Skin Lesion Classification -- 2.2 Correlation Mining -- 3 Methodology -- 3.1 Feature Enhancement Stage -- 3.2 Label Distribution Learning Stage -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Hyper Parameters Setting -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Studies -- 5 Conclusion -- References -- CheXNet: Combing Transformer and CNN for Thorax Disease Diagnosis from Chest X-ray Images -- 1 Introduction -- 2 Related Work -- 2.1 Label Dependency and Imbalance -- 2.2 Extensive Lesion Location -- 3 Approaches -- 3.1 Label Embedding and MSP Block -- 3.2 Inner Branch -- 3.3 C2T and T2C in IIM -- 4 Experiments -- 4.1 Dataset -- 4.2 Comparison to the State-of-the-Arts -- 4.3 Ablation Study -- 5 Conclusion -- References -- Cross Attention Multi Scale CNN-Transformer Hybrid Encoder Is General Medical Image Learner -- 1 Introduction -- 2 Methods -- 2.1 Dual Encoder -- 2.2 Shallow Fusion Module -- 2.3 Deep Fusion Module -- 2.4 Deep Supervision -- 3 Experiments and Results -- 3.1 Dateset -- 3.2 Implementation Details -- 3.3 Comparison with Other Methods -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Weakly/Semi-supervised Left Ventricle Segmentation in 2D Echocardiography with Uncertain Region-Aware Contrastive Learning -- 1 Introduction -- 2 Methods -- 2.1 Multi-level Regularization of Semi-supervision -- 2.2 Uncertain Region-Aware Contrastive Learning -- 2.3 Differentiable Ejection Fraction Estimation of Weak Supervision -- 3 Datasets and Implementation Details -- 4 Results -- 5 Conclusion -- References. Spatial-Temporal Graph Convolutional Network for Insomnia Classification via Brain Functional Connectivity Imaging of rs-fMRI -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Data Augmentation -- 3.3 Construction of Spatio-Temporal Graph -- 3.4 Spatio-Temporal Graph Convolution (ST-GC) -- 3.5 ST-GCN Building -- 3.6 Edge Importance Learning -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Analysis of Different Sliding Window Step Size -- 4.4 Comparison with Other Methods -- 5 Conclusion -- References -- Probability-Based Nuclei Detection and Critical-Region Guided Instance Segmentation -- 1 Introduction -- 2 Related Works on Nucleus Instance Segmentation -- 2.1 Bounding Box-Based Methods -- 2.2 Boundary-Based Methods -- 2.3 Critical Region-Based Methods -- 3 CGIS Method and CPF Feature -- 3.1 Critical-Region Guided Instance Segmentation -- 3.2 Central Probability Field -- 3.3 Nuclear Classification -- 4 Experimental Verification and Analysis -- 4.1 Datasets and Evaluation Metrics -- 4.2 Parameters and Implementation Details -- 4.3 Comparisons with Other Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- FlashViT: A Flash Vision Transformer with Large-Scale Token Merging for Congenital Heart Disease Detection -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 FlashViT Block -- 2.3 Large-Scale Token Merging Module -- 2.4 Architecture Variants -- 3 Experiments -- 3.1 CHD Dataset -- 3.2 Evaluations on CHD Dataset -- 3.3 Homogenous Pre-training Strategy -- 3.4 Ablation Study -- 4 Conclusion -- References -- Semi-supervised Retinal Vessel Segmentation Through Point Consistency -- 1 Introduction -- 2 Method -- 2.1 Segmentation Module -- 2.2 Point Consistency Module -- 2.3 Semi-supervised Training Through Point Consistency -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details. 3.3 Experimental Results -- 4 Conclusion -- References -- Knowledge Distillation of Attention and Residual U-Net: Transfer from Deep to Shallow Models for Medical Image Classification -- 1 Introduction -- 2 Methods -- 2.1 Res-Transformer Teacher Model Based on U-Net Structure -- 2.2 ResU-Net Student Model Incorporates Residual -- 2.3 Knowledge Distillation -- 3 Data and Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Results -- 4 Conclusion -- References -- Two-Stage Deep Learning Segmentation for Tiny Brain Regions -- 1 Introduction -- 2 Method -- 2.1 Overall Workflow -- 2.2 Two-Stage Segmentation Network -- 2.3 Contrast Loss Function -- 2.4 Attention Modules -- 3 Experiments -- 3.1 Dataset and Metrics -- 3.2 Comparisons Experiments -- 4 Conclusion -- References -- Encoder Activation Diffusion and Decoder Transformer Fusion Network for Medical Image Segmentation -- 1 Introduction -- 2 Methodology -- 2.1 Lightweight Convolution Modulation -- 2.2 Encoder Activation Diffusion -- 2.3 Multi-scale Decoding Fusion with Transformer -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Evaluation Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Liver Segmentation via Learning Cross-Modality Content-Aware Representation -- 1 Introduce -- 2 Methodology -- 2.1 Overview -- 2.2 Image-to-Image Network -- 2.3 Peer-to-Peer Network -- 3 Experiments -- 3.1 Dataset -- 3.2 Setting -- 3.3 Result -- 4 Conclusion -- References -- Semi-supervised Medical Image Segmentation Based on Multi-scale Knowledge Discovery and Multi-task Ensemble -- 1 Introduction -- 2 Related Works on SSMIS -- 3 Proposed Method -- 3.1 Multi-scale Knowledge Discovery -- 3.2 Multi-task Ensemble Strategy -- 4 Experiments and Analysis -- 4.1 Datasets and Implementation Details -- 4.2 Comparisons with State-of-the-Art Methods -- 4.3 Ablation Studies. 5 Conclusion -- References -- LATrans-Unet: Improving CNN-Transformer with Location Adaptive for Medical Image Segmentation -- 1 Introduction -- 2 Method -- 2.1 Encoder-Decoder Architecture -- 2.2 Location-Adaptive Attention -- 2.3 SimAM-Skip Structure -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Evaluation Results -- 3.4 Ablation Study -- 3.5 Discussion -- 4 Conclusions -- References -- Adversarial Keyword Extraction and Semantic-Spatial Feature Aggregation for Clinical Report Guided Thyroid Nodule Segmentation -- 1 Introduction -- 2 Method -- 2.1 Adversarial Keyword Extraction (AKE) -- 2.2 Semantic-Spatial Features Aggregation (SSFA) -- 2.3 The Full Objective Functions -- 3 Experiment -- 3.1 Comparison with the State-of-the-Arts -- 3.2 Ablation Study -- 3.3 Visualization of Generated Keyword Masks -- 4 Conclusion -- References -- A Multi-modality Driven Promptable Transformer for Automated Parapneumonic Effusion Staging -- 1 Introduction -- 2 Related Works -- 2.1 Disease Detection Methods with CT Images -- 2.2 Classification Methods with Time Sequence Videos -- 3 Method -- 3.1 CNN-Based Slice-Level Feature Extraction -- 3.2 Prompt Encoder -- 3.3 Cross-Modality Fusion Transformer -- 4 Experiments -- 4.1 Setting and Implementation -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Assessing the Social Skills of Children with Autism Spectrum Disorder via Language-Image Pre-training Models -- 1 Introduction -- 2 Related Works -- 2.1 Behavior Signal Processing System -- 2.2 Language-Image Pre-training Models -- 3 Methodology -- 3.1 Paradigm Design -- 3.2 Language-Image Based Method -- 4 Experimental Results -- 4.1 Database -- 4.2 Results -- 4.3 Discussion -- 5 Conclusion -- References -- PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision -- 1 Introduction -- 2 Method. 2.1 Overview. |
| Record Nr. | UNINA-9910799224803321 |
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Liu Qingshan
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VII / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part VII / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (525 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer networks Computer systems Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9985-40-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Document Analysis and Recognition -- Feature Extraction and Feature Selection -- Multimedia Analysis and Reasoning. |
| Record Nr. | UNINA-9910799213803321 |
|
Liu Qingshan
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IX / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IX / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (520 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer networks Computer systems Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9985-46-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part IX -- Neural Network and Deep Learning II -- Decoupled Contrastive Learning for Long-Tailed Distribution -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Online Tail Samples Discovery -- 3.2 Hard Negatives Generation -- 3.3 Contrastive Loss Reweighting -- 4 Experiments -- 4.1 Linear Probing Evaluations -- 4.2 Analysis -- 5 Conclusion -- References -- MFNet: A Channel Segmentation-Based Hierarchical Network for Multi-food Recognition -- 1 Introduction -- 2 Related Work -- 3 Food Image Datasets Construction -- 3.1 Food Images Collection -- 3.2 Annotation and Statistics -- 4 Method -- 4.1 CWF: Channel-Level Whole Image Food Information Acquisition -- 4.2 SGC: Spatial-Level Global Information Constraints -- 4.3 SPF: Spatial-Level Part Image Food Information Acquisition -- 5 Experiments -- 5.1 Datasets and Evaluation Metrics -- 5.2 Implementation Details -- 5.3 Performance Comparison with Other Method -- 5.4 Ablation Study -- 5.5 Visualization Result -- 6 Conclusion -- References -- Improving the Adversarial Robustness of Object Detection with Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Attacks -- 2.2 Adversarial Defenses -- 2.3 Contrastive Learning -- 3 Proposed Method -- 3.1 Contrastive Learning Module -- 3.2 Contrastive Adversarial SSD -- 3.3 Contrastive Adversarial YOLO -- 3.4 Adversarial Training with Contrastive Learning -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Defense Capability Evaluation -- 5 Conclusion -- References -- CAWNet: A Channel Attention Watermarking Attack Network Based on CWABlock -- 1 Introduction -- 2 Related Work -- 2.1 Attacked Watermarking Algorithm -- 2.2 Watermarking Attack Techniques -- 3 The Proposed Method -- 3.1 CAWNet -- 3.2 Attention Mechanism -- 3.3 CWABlock -- 4 Experiments and Results Analysis.
4.1 Evaluation Criteria -- 4.2 Ablation Experiment -- 4.3 Effects of Traditional Attack and Different Deep Learning Attack Methods -- 4.4 Stability and Suitability Testing -- 5 Conclusion -- References -- Global Consistency Enhancement Network for Weakly-Supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Middle-Level Feature Auxiliary -- 3.2 Intra-class Consistency Enhancement -- 3.3 Critical Region Suppression -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 CAM Performance -- 4.4 Segmentation Performance -- 4.5 Ablation Study -- 5 Conclusion -- References -- Enhancing Model Robustness Against Adversarial Attacks with an Anti-adversarial Module -- 1 Introduction -- 2 Related Works -- 2.1 Gradient Masking -- 2.2 Adversarial Examples Detection -- 2.3 Robust Optimization -- 3 Methods -- 3.1 Counter-Adversarial Module -- 3.2 Enhancing Defense Against Black-Box Attacks -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- FGPTQ-ViT: Fine-Grained Post-training Quantization for Vision Transformers -- 1 Introduction -- 2 Related Work -- 2.1 CNN Quantization -- 2.2 Vision Transformer Quantization -- 3 Method -- 3.1 FGPTQ-ViT Framework -- 3.2 Fine-Grained ViT Quantization -- 3.3 Adaptive Piecewise Point Search Algorithm -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Learning Hierarchical Representations in Temporal and Frequency Domains for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional and Transformer Models -- 2.2 Fourier Transform and Decomposition Models -- 3 Proposed Approach -- 3.1 Time Series Hierarchical Decomposition -- 3.2 Trend Forecasting Module -- 3.3 Seasonal Forecasting Module -- 4 Experiments. 4.1 Dataset -- 4.2 Baselines and Setup -- 4.3 Implement Details and Evaluation Metrics -- 4.4 Main Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- DeCAB: Debiased Semi-supervised Learning for Imbalanced Open-Set Data -- 1 Introduction -- 2 Related Work -- 2.1 General SSL Methods -- 2.2 Imbalanced SSL Methods -- 2.3 Open-Set SSL Methods -- 3 Proposed Method -- 3.1 Problem Setting and Notations -- 3.2 Class-Aware Threshold -- 3.3 Selective Sample Reweighting -- 3.4 Positive-Pair Reweighting -- 3.5 Overall Training Objective -- 4 Experimental Results -- 4.1 Experimental Settings -- 4.2 Numerical Comparison -- 4.3 Analysis on Impact of OOD Data -- 4.4 Ablation Experiments -- 5 Conclusion -- A Analysis of the Effect of OOD Data to SSL Methods -- B Algorithm Flowchart -- C Visualized Comparison -- References -- An Effective Visible-Infrared Person Re-identification Network Based on Second-Order Attention and Mixed Intermediate Modality -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Second-Order Attention Module -- 3.2 Mixed Intermediate Modality Module -- 3.3 Optimization -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- Quadratic Polynomial Residual Network for No-Reference Image Quality Assessment -- 1 Introduction -- 2 Related Work -- 2.1 IQA and Information Entropy -- 2.2 IQA and Deep Learning -- 3 Design of Network -- 3.1 Two-Dimensional Information Entropy for Patch Sampling -- 3.2 Network Architecture -- 4 Experiment Result -- 5 Conclusion -- References -- Interactive Learning for Interpretable Visual Recognition via Semantic-Aware Self-Teaching Framework -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Patch Selection Strategy -- 3.2 Semantic-Aware Self-Teaching -- 4 Experiments. 5 Conclusion -- References -- Adaptive and Compact Graph Convolutional Network for Micro-expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Structure Graph -- 3 Method -- 3.1 Cheek Included Facial Graph -- 3.2 Tightly Connected Strategy -- 3.3 Small Region Module -- 3.4 Adaptive and Compact Graph Convolutional Network -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Quantitative Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Consistency Guided Multiview Hypergraph Embedding Learning with Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI -- 1 Introduction -- 2 Methods -- 2.1 Hypergraph and Hypergraph Construction with FCN -- 2.2 Proposed CG-MHGEL with Multiatlas-Based FCNs -- 3 Experiment -- 3.1 Experimental Settings -- 3.2 Experimental Results and Analysis -- 4 Conclusion -- References -- A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Framework -- 3.3 Diffusion Simulation User Behavior Embedding -- 3.4 User Behavior Fusion Transformer -- 3.5 Cascade Perception Attention Network -- 3.6 Diffusion Prediction -- 4 Experiment -- 4.1 Results -- 5 Conclusion -- References -- A Representation Learning Link Prediction Approach Using Line Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The NLG-GNN Framework -- 4 Experimental Setup -- 4.1 Experimental Results -- 5 Conclusion -- References -- Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-Based Data -- 1 Introduction -- 2 Related Work -- 2.1 Graph Representations -- 2.2 Dynamic Graph Neural Network -- 3 Methods -- 3.1 Local Self Attention -- 3.2 Global Temporal Attention -- 4 Experiments -- 4.1 Datasets. 4.2 Setup -- 4.3 Continuous Data Inductive Learning -- 4.4 Discrete Data Inductive Learning -- 4.5 Discrete Data Transductive Learning -- 5 Conclusion -- References -- Federated Learning Based on Diffusion Model to Cope with Non-IID Data -- 1 Introduction -- 2 Method -- 2.1 The First Stage -- 2.2 The Second Stage -- 2.3 The Third Stage -- 3 Experiments -- 3.1 Setup -- 3.2 Performance Comparison -- 3.3 Experimental Factors Analysis -- 4 Conclusion -- References -- SFRSwin: A Shallow Significant Feature Retention Swin Transformer for Fine-Grained Image Classification of Wildlife Species -- 1 Introduction -- 2 Related Works -- 2.1 Convolutional Neural Network -- 2.2 Vision Transformer -- 3 Methodology -- 3.1 Self-attentive Mechanism Based on Shifted Windows -- 3.2 Random Data Enhancement -- 4 Evaluation -- 4.1 Datasets and Implementation Details -- 4.2 Model Complexity Analysis -- 5 Conclusion -- References -- A Robust and High Accurate Method for Hand Kinematics Decoding from Neural Populations -- 1 Introduction -- 2 Related Works -- 2.1 iBMI Cortical Control Decoding Algorithm -- 2.2 Attention Module -- 3 Method -- 3.1 Neural Recording System and Behavioral Task -- 3.2 Experimental Procedure of the Cortical Control -- 3.3 Temporal-Attention QRNN -- 3.4 Evaluation Metrics -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Comparison of Decoding Results -- 4.3 Discussion -- 5 Conclusion -- References -- Multi-head Attention Induced Dynamic Hypergraph Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Neural Networks on Graph -- 2.2 Neural Networks on Hypergraph -- 3 Methodology -- 3.1 Definitions and Notations -- 3.2 Hypergraph Construction -- 3.3 Vertex Convolution -- 3.4 Hyperedge Convolution -- 3.5 The Proposed Algorithm -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Results and Discussion. 4.4 Ablation Studies. |
| Record Nr. | UNINA-9910799208803321 |
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Liu Qingshan
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji
| Pattern Recognition and Computer Vision [[electronic resource] ] : 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13–15, 2023, Proceedings, Part IV / / edited by Qingshan Liu, Hanzi Wang, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang, Rongrong Ji |
| Autore | Liu Qingshan |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (514 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
WangHanzi
MaZhanyu ZhengWeishi ZhaHongbin ChenXilin WangLiang JiRongrong |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Artificial intelligence Application software Computer networks Computer systems Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence Computer and Information Systems Applications Computer Communication Networks Computer System Implementation Machine Learning |
| ISBN | 981-9984-62-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pattern Classification and Cluster Analysis -- Performance Evaluation and Benchmarks -- Remote Sensing Image Interpretation. |
| Record Nr. | UNISA-996587868403316 |
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Liu Qingshan
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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