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[null,null,"Titolo",null,"Advances in computer graphics : 39th computer graphics international conference, CGI 2022, virtual event, September 12-16, 2022, proceedings / / Nadia Magnenat-Thalmann [and six others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-23473-1"]
[null,null,"Descrizione fisica",null,"1 online resource (590 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science ; ; Volume 13443"]
[null,null,"Disciplina",null,"006.6869"]
[null,null,"Soggetti",null,"Computer graphics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Image Analysis and Processing -- Multi-granularity Feature Attention Fusion Network for Image-Text Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 2.1 Text Sentiment Analysis -- 2.2 Image Sentiment Analysis -- 2.3 Multimodal Sentiment Analysis -- 3 Our Proposed Model -- 3.1 Model Overview -- 3.2 Feature Learning Layer -- 3.3 Interactive Information Fusion Layer -- 3.4 Interactive Information Fusion Layer -- 4 Experimental Results -- 4.1 Dataset Description -- 4.2 Baselines -- 4.3 Experimental Results and Analysis -- 5 Conclusions -- References -- Toward Efficient Image Denoising: A Lightweight Network with Retargeting Supervision Driven Knowledge Distillation*-12pt -- 1 Introduction -- 2 Proposed Method -- 2.1 Lightweight U-shaped Denoising Network: LUNet -- 2.2 Retargeting Supervision Driven Knowledge Distillation -- 3 Experimental Results -- 3.1 Experimental Settings -- 3.2 Quantitative and Qualitative Results -- 3.3 Ablation Studies -- 4 Conclusions -- References -- A Pig Pose Estimation Model for Measuring Pig's Body Size -- 1 Introduction -- 2 Related Work -- 2.1 Measuring Animal Body Size -- 2.2 Key Point Identification -- 2.3 Attention Mechanism -- 3 Our Approach -- 3.1 Our Network Header -- 3.2 Down-Sampling Module -- 3.3 Selection of Key Points -- 4 Experiments -- 4.1 Dataset -- 4.2 Results on Image and Video -- 4.3 Results on Combining Attention Mechanism -- 4.4 Results on Using Our Network Header -- 5 Conclusion -- References -- Topology-Aware Learning for Semi-supervised Cross-domain Retinal Artery/Vein Classification*-12pt -- 1 Introduction -- 2 Method -- 2.1 Overview -- "]
[{"$nulls":4},"2.2 Spatial Transformation Module -- 2.3 Regional Mixing Module -- 2.4 Loss Function -- 3 Experimental Results -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Artery/Vein Classification Performance."]
[{"$nulls":4},"4 Conclusion -- References -- Face Super-Resolution with Better Semantics and More Efficient Guidance -- 1 Introduction -- 2 Method -- 2.1 Overview of the Proposed Framework -- 2.2 Better Semantic Prior -- 2.3 More Efficient Guidance -- 2.4 Loss Functions -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Comparisons with the State-of-the-Arts -- 3.3 Ablation Study -- 4 Conclusion -- References -- Graphs and Networks -- Layout and Display of Network Graphs on a Sphere*-12pt -- 1 Introduction -- 2 Related Work -- 3 Visualization Pipeline -- 4 Graph Visualization Algorithms -- 4.1 Layout - Adaptation of Fruchterman-Reingold Algorithm -- 4.2 Quantitative Evaluation -- 4.3 Visualizing Spheres in Paraview -- 4.4 Mathematical Basis for Visualizing Arcs in Paraview -- 5 Browser-Based Visualization -- 6 Conclusion -- References -- Joint Matrix Factorization and Structure Preserving for Domain Adaptation*-12pt -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Notations -- 3.2 Problem Formulation -- 3.3 Optimization -- 4 Experiments -- 4.1 Datasets Description -- 4.2 Results -- 4.3 Parameter Sensitivity and Ablation Study -- 5 Conclusion -- References -- Graph Adversarial Network with Bottleneck Adapter Tuning for Sign Language Production*-12pt -- 1 Introduction -- 2 Related Work -- 2.1 Sign Language Production -- 2.2 Transfer Learning -- 2.3 Graph Convolutional Network -- 3 Method -- 3.1 Adapter-Based Contextual Representation -- 3.2 Channel-Wise Target Length Predictor -- 3.3 Cross-modal Sign Pose Generator -- 3.4 Spatial-Temporal Graph Convolutional Discriminator -- 3.5 Loss Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Training Settings -- 4.3 Evaluation Metrics -- 4.4 Quantitative Evaluation -- 4.5 Visual Comparison -- 4.6 Ablation Study -- 5 Conclusion -- References -- Estimation and Feature Matching."]
[{"$nulls":4},"Facial Landmarks Based Region-Level Data Augmentation for Gaze Estimation*-12pt -- 1 Introduction -- 2 Related Works -- 2.1 Gaze Estimation -- 2.2 Data Augmentation -- 3 Proposed Method -- 3.1 Facial Landmarks Based Region Mask -- 3.2 Non-eye Regions Data Augmentation -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Performance Comparison -- 4.3 Ablation Studies -- 5 Conclusions -- References -- An Efficient Dense Depth Map Estimation Algorithm Using Direct Stereo Matching for Ultra-Wide-Angle Images*-12pt -- 1 Introduction -- 2 Patch Matching Stereo -- 2.1 Camera Projection Model -- 2.2 Patch Matching Stereo for Ultra-Wide-Angle Images -- 3 Experimental Results -- 3.1 The TUM VI Benchmark -- 3.2 Comparisons -- 3.3 The Oxford RobotCar Dataset -- 4 Conclusion -- References -- Ad-RMS: Adaptive Regional Motion Statistics for Feature Matching Filtering -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Adaptive Regional Division -- 3.2 Regional Motion Statistics -- 4 Experiment -- 4.1 Evaluation -- 4.2 Dataset and Input Data -- 4.3 Parameters -- 4.4 Comparative Analysis of Results -- 5 Summary -- References -- 3D Reconstruction -- Visual Indoor Navigation Using Mobile Augmented Reality -- 1 Introduction -- 2 Related Work -- 2.1 Indoor Navigation System -- 2.2 Positioning and Navigation Technology -- 3 Research Content and Methodology -- 3.1 Plane and Point Cloud Detection and Visualization -- 3.2 Indoor Map Construction -- 3.3 Map Download and Self-positioning -- 3.4 Path Planning and Display -- 4 Experiment -- 5 Analysis of Results -- References -- Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo -- 1 Introduction -- 2 Related Work -- 2.1 Coarse-to-Fine MVS Methods -- 2.2 Depth Sampling "]
[{"$nulls":4},"Range -- 2.3 Cost Volume -- 3 Methods -- 3.1 Overview -- 3.2 Depth Sampling Range Estimation -- 3.3 Supervise on Cost Volume."]
[{"$nulls":4},"3.4 Loss Function -- 4 Experiment -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results on DTU Dataset -- 4.4 Results on BlendedMVS -- 4.5 Effectiveness of Multi-strategies -- 4.6 Ablation Study -- 5 Conclusion -- References -- Reconstructing the Surface Mesh Representation for Single Neuron -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Branch Identification -- 3.2 Surface Reconstruction of Neurites -- 3.3 Surface Reconstruction of the Soma -- 4 Experiments and Results -- 5 Conclusion -- References -- WEmap: Weakness-Enhancement Mapping for 3D Reconstruction with Sparse Image Sequences -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Initial Reconstruction -- 3.2 Generation of the Enhanced Set -- 3.3 Reconstruction with Weighted Samples -- 4 Experiments -- 4.1 Datasets -- 4.2 Experiments on the Sparse DTU Dataset -- 4.3 Experiments on the Sparse Tanks and Temples Dataset -- 5 Conclusion -- References -- Rendering and Animation -- Comparing Traditional Rendering Techniques to Deep Learning Based Super-Resolution in Fire and Smoke Animations -- 1 Introduction -- 2 Related Work -- 3 TecoGAN -- 3.1 Network Architecture for Video Super-Resolution -- 3.2 Usage -- 4 Dataset Generation -- 4.1 Creating a Smoke Simulation -- 4.2 Multiple Simulations -- 5 Evaluation -- 5.1 Creating Different Renders -- 5.2 Comparing Video Quality -- 5.3 Cross Simulation Comparison -- 6 Conclusion -- References -- Real-Time Light Field Path Tracing -- 1 Introduction -- 2 Related Work -- 3 General Pipeline and Our Implementation -- 3.1 General Pipeline -- 3.2 Our Implementation -- 4 Experiments -- 5 Results -- 5.1 Runtime -- 5.2 Quality -- 6 Conclusions -- References -- Crowd Simulation with Detailed Body Motion and Interaction -- 1 Introduction -- 2 Related Work -- 2.1 Crowd Simulation -- 2.2 Motion Matching -- 3 Overview -- 4 Hierarchical Control."]
[{"$nulls":4},"5 Agent Model -- 5.1 Visual System -- 5.2 Blackboard System -- 5.3 Decision System -- 5.4 Animation System -- 6 Co-simulation System -- 6.1 Mode Switch -- 6.2 Inverse Kinematic -- 7 Results -- 7.1 Behaviour Simulation -- 7.2 Animation Simulation -- 7.3 User Study -- 8 Conclusion and Future Work -- References -- Towards Rendering the Style of 20th Century Cartoon Line Art in 3D Real-Time -- 1 Introduction -- 2 Related Works -- 2.1 Contour Extraction -- 2.2 Line Stylisation -- 2.3 Offline Rendering -- 3 Approaches -- 3.1 Edge Marking -- 3.2 Geometry Shader Line Rendering -- 3.3 Line Weight Map -- 3.4 Editing the Line Thickness in Real-Time -- 3.5 Recording Line Thickness in Real-Time Animation -- 4 Results -- 5 Conclusion and Future Works -- References -- Detection and Recognition -- Face Detection Algorithm in Classroom Scene Based on Deep Learning -- 1 Introduction -- 2 Related Works -- 2.1 Overview of Target Detection -- 2.2 Optical Flow Method -- 3 Proposed Method -- 3.1 Model Overview -- 3.2 Network Design -- 3.3 Improved Seq NMS -- 3.4 Face Count -- 4 Experience -- 4.1 Dataset -- 4.2 Pretreatment -- 4.3 Parameter Setting -- 4.4 Result -- 5 Conclusion -- References -- GRVT: Toward Effective Grocery Recognition via Vision Transformer -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Vision Transformer -- 3.2 GRVT Architecture -- 3.3 Multi-scale Patch Embedding -- 3.4 Mixed Attention Selection -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Comparison with the State of the Art -- 4.3 Ablation Study -- 4.4 Visualization -- 5 Conclusion -- References -- A Transformer-Based Cloth-Irrelevant Patches Feature Extracting Method for Long-Term Cloth-Changing Person Re-identification -- 1 Introduction -- 2 Related Work -- 2.1 Cloth-Changing Person Re-"]
[{"$nulls":4},"identification -- 2.2 Vision Transformer -- 2.3 Human Parsing -- 3 Methodology."]
[{"$nulls":4},"3.1 Transformer Encoder."]