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Biblioteca

MARC Lista (tabellare)
Advances in Image and Graphics Technologies : 10th Chinese Conference, IGTA 2015, Beijing, China, June 19-20, 2015, Proceedings / / edited by Tieniu Tan, Qiuqi Ruan, Shengjin Wang, Huimin Ma, Kaichang Di
Advances in Image and Graphics Technologies : 10th Chinese Conference, IGTA 2015, Beijing, China, June 19-20, 2015, Proceedings / / edited by Tieniu Tan, Qiuqi Ruan, Shengjin Wang, Huimin Ma, Kaichang Di
Edizione [1st ed. 2015.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015
Descrizione fisica 1 online resource (XII, 456 p. 270 illus.)
Disciplina 006.6
Collana Communications in Computer and Information Science
Soggetto topico Optical data processing
Pattern recognition
Artificial intelligence
Computer graphics
ISBN 3-662-47791-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Image processing technology -- Image analysis und understanding -- Computer vision and pattern recognition -- Big data mining -- Computer graphics and VR -- Image technology application.
Record Nr. UNINA-9910299243203321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advances in Image and Graphics Technologies : Chinese Conference, IGTA 2014, Beijing, China, June 19-20, 2014. Proceedings / / edited by Tieniu Tan, Qiuqi Ruan, Shengjin Wang, Huimin Ma, Kaiqi Huang
Advances in Image and Graphics Technologies : Chinese Conference, IGTA 2014, Beijing, China, June 19-20, 2014. Proceedings / / edited by Tieniu Tan, Qiuqi Ruan, Shengjin Wang, Huimin Ma, Kaiqi Huang
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (X, 360 p. 249 illus.)
Disciplina 006.6
Collana Communications in Computer and Information Science
Soggetto topico Optical data processing
Artificial intelligence
Computers
Coding theory
Information theory
Computer simulation
ISBN 3-662-45498-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Image processing and graphics and related topics -- Object detection -- Pattern recognition -- Object tracking -- Classification -- Image segmentation -- Reconstruction.
Record Nr. UNINA-9910298966803321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advances in Image and Graphics Technologies : Chinese Conference, IGTA 2013, Beijing, China, April 2-3, 2013. Proceedings / / edited by Tieniu Tan, Qiuqi Ruan, Xilin Chen, Huimin Ma, Liang Wang
Advances in Image and Graphics Technologies : Chinese Conference, IGTA 2013, Beijing, China, April 2-3, 2013. Proceedings / / edited by Tieniu Tan, Qiuqi Ruan, Xilin Chen, Huimin Ma, Liang Wang
Edizione [1st ed. 2013.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013
Descrizione fisica 1 online resource (XII, 340 p. 196 illus.)
Disciplina 006.6
006.37
Collana Communications in Computer and Information Science
Soggetto topico Optical data processing
Pattern recognition
Computer graphics
Soggetto genere / forma Conference papers and proceedings.
Soggetto non controllato Graphics technologies
IGTA
ISBN 3-642-37149-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Real-Time Non-invasive Imaging of Subcutaneous Blood Vessels -- The Electronic Countermeasures Optimization Based on PCA and Multiple Regression Analysis -- A Digital Watermarking Algorithm Based on EHD Image Feature Analysis -- TLD Based Visual Target Tracking for Planetary Rover Exploration -- Ocean Surface Rendering Using Shader Technologies -- The ART2 Neural Network Based on the Adaboost Rough Classification -- An Unsymmetrical Diamond Search Algorithm for H.264/AVC Motion Estimation -- A New Fusion Method of Palmprint and Palmvein -- A Fatigue Testing Method Based on Machine Vision.
Record Nr. UNINA-9910437572203321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Pattern recognition and computer vision . Part III : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pattern recognition and computer vision . Part III : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , 2022
Descrizione fisica 1 online resource (648 pages)
Disciplina 006.4
Collana Lecture notes in computer science
Soggetto topico Pattern recognition systems
Computer vision
ISBN 3-030-88010-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part III -- Low-Level Vision and Image Processing -- SaliencyBERT: Recurrent Attention Network for Target-Oriented Multimodal Sentiment Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Task Definition -- 3.2 Recurrent Attention Network -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results and Analysis -- 5 Conclusion -- References -- Latency-Constrained Spatial-Temporal Aggregated Architecture Search for Video Deraining -- 1 Introduction -- 2 The Proposed Method -- 2.1 Spatial-Temporal Aggregated Architecture -- 2.2 Architecture Search -- 3 Experimental Results -- 3.1 Experiment Preparation -- 3.2 Running Time Evaluation -- 3.3 Quantitative Comparison -- 3.4 Qualitative Comparison -- 3.5 Ablation Study -- 4 Conclusions -- References -- Semantic-Driven Context Aggregation Network for Underwater Image Enhancement -- 1 Introduction -- 2 Method -- 2.1 The Overall Architecture -- 2.2 Semantic Feature Extractor -- 2.3 Multi-scale Feature Transformation Module -- 2.4 Context Aggregation Enhancement Network and Loss Function -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Comparison with the State-of-the-Arts -- 3.3 Ablation Study -- 3.4 Application on Salient Object Detection -- 4 Conclusion -- References -- A Multi-resolution Medical Image Fusion Network with Iterative Back-Projection -- 1 Introduction -- 2 Proposed Approach -- 2.1 Overall Framework -- 2.2 Network Architecture -- 2.3 Loss Function -- 3 Experiments -- 3.1 Dataset and Training Details -- 3.2 Results and Analysis of IBPNet -- 4 Conclusions -- References -- Multi-level Discriminator and Wavelet Loss for Image Inpainting with Large Missing Area -- 1 Introduction -- 2 Related Work -- 2.1 Image Inpainting -- 2.2 Adversarial Training -- 3 Our Approach -- 3.1 Multi-level Discriminator -- 3.2 Wavelet Loss.
4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 5 Conclusion -- References -- 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement -- 1 Introduction -- 2 Related Work -- 2.1 Low-Light Image Enhancement -- 2.2 Low-Light Video Enhancement -- 3 Method -- 3.1 Problem Formulation -- 3.2 Overview of the Pipeline -- 3.3 RSTAB Module -- 3.4 Unet Architecture with Global Projection -- 4 Experiment -- 4.1 Experimental Setting -- 4.2 Comparison with State-of-the-art Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- Single Image Specular Highlight Removal on Natural Scenes -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Scene Illumination Evaluation -- 3.2 Smooth Feature Extraction -- 3.3 Coefficient Estimation and Highlight Removal -- 4 Experiments -- 4.1 Quantitative Comparison on Laboratory Images -- 4.2 Visual Effect Comparison on Natural Scene Images -- 4.3 Discussion of Important Parameters -- 5 Conclusion -- References -- Document Image Binarization Using Visibility Detection and Point Cloud Segmentation -- 1 Introduction -- 2 TPO(Target-Point Occlusion) -- 2.1 Point Cloud Transformation -- 2.2 Convex Hull -- 3 Algorithm -- 3.1 Binarization -- 3.2 Unshadow Binarization -- 4 Experiment -- 5 Conclusion -- References -- LF-MAGNet: Learning Mutual Attention Guidance of Sub-Aperture Images for Light Field Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Light Field Image Super-Resolution -- 2.2 Visual Attention Mechanism -- 3 Proposed Method -- 3.1 Shallow Feature Extraction -- 3.2 Mutual Attention Guidance -- 3.3 LF Image Reconstruction -- 4 Experiment -- 4.1 Dataset and Implementation Details -- 4.2 Ablation Studies -- 4.3 Comparisons with the State-of-The-Arts -- 5 Conclusion -- References.
Infrared Small Target Detection Based on Weighted Variation Coefficient Local Contrast Measure -- 1 Introduction -- 2 The Proposed Algorithm -- 2.1 Variation Coefficient Local Contrast Measure -- 2.2 Weighted Variation Coefficient Local Contrast Measure -- 2.3 Target Detection -- 3 Experimental Results -- 3.1 Enhancement Performance Comparison -- 3.2 Detection Performance Comparison -- 4 Conclusion -- References -- Scale-Aware Distillation Network for Lightweight Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Scale-Aware Distillation Block -- 3.3 Comparisons with Other Information Distillation Methods -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with the State-of-the-Arts -- 5 Conclusion -- References -- Deep Multi-Illumination Fusion for Low-Light Image Enhancement -- 1 Introduction -- 2 Deep Multi-Illumination Fusion -- 2.1 Network Structure -- 2.2 Loss Function -- 3 Experimental Results -- 3.1 Implementation Details -- 3.2 Performance Evaluation -- 3.3 Ablation Analysis -- 3.4 Object Instance Segmentation -- 4 Conclusion -- References -- Relational Attention with Textual Enhanced Transformer for Image Captioning -- 1 Introduction -- 2 Related Work -- 2.1 Relationship Exploration -- 2.2 Transformer Architecture -- 3 The Proposed Approach -- 3.1 Relation Module -- 3.2 Attention Module -- 3.3 Decoder Module -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with State-of-the-Art -- 5 Conclusion -- References -- Non-local Network Routing for Perceptual Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Non-local Network Routing -- 3.2 Model Learning -- 4 Experiments -- 4.1 Evaluation Dataset and Metric.
4.2 Implementation Details -- 4.3 Derived Architecture -- 4.4 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Multi-focus Image Fusion with Cooperative Image Multiscale Decomposition -- 1 Introduction -- 2 Cooperative Image Multiscale Decomposition Based MGF -- 2.1 Mutually Guided Filter -- 2.2 Cooperative Image Multiscale Decomposition -- 3 Image Fusion with CIMD -- 3.1 Base Layers Fusion -- 3.2 Detailed Layers Fusion -- 3.3 Reconstruction -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Comparison to Classical Fusion Method -- 5 Conclusions -- References -- An Enhanced Multi-frequency Learned Image Compression Method -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Formulation of Multi-frequency Learned Compression Models -- 3.2 Channel Attention Scheme -- 3.3 Decoder-Side Enhancement -- 4 Experiment Results -- 4.1 Parameter Description -- 4.2 Results Evaluation -- 5 Conclusion -- References -- Noise Map Guided Inpainting Network for Low-Light Image Enhancement -- 1 Introduction -- 2 Related Works -- 2.1 Low-Light Image Enhancement -- 2.2 Image Inpainting -- 3 Method -- 3.1 Stage I: Decomposition -- 3.2 Stage II: Restoration -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusion -- References -- FIE-GAN: Illumination Enhancement Network for Face Recognition -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Architecture -- 3.2 Loss Function -- 3.3 Deployment in Face Recognition -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Visual Perception Results -- 4.3 Face Recognition Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Illumination-Aware Image Quality Assessment for Enhanced Low-Light Image -- 1 Introduction -- 2 Illumination-Aware Quality Assessment of Enhanced Low-Light Image -- 2.1 Intrinsic Decomposition Module.
2.2 CNN-based Feature Extraction Module -- 2.3 Learnable Perceptual Distance Measurement -- 3 Experiments -- 3.1 Basic Evaluations -- 3.2 Evaluations of LIE-IQA with Related Methods -- 3.3 LIE-IQA for Low-Light Enhancement -- 4 Conclusion -- References -- Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution -- 1 Introduction -- 2 Tensor Notations and Preliminaries -- 3 Problem Formulation -- 3.1 Problem Description and Degradation Model -- 3.2 MAP Formulation -- 3.3 Smooth Coupled Tucker Decomposition Model -- 3.4 Optimization -- 4 Experimental Results -- 4.1 Experimental Settings and Implementation Issues -- 4.2 Experimental Results -- 4.3 Choice of Model Order -- 5 Conclusion -- 1. References -- Self-Supervised Video Super-Resolution by Spatial Constraint and Temporal Fusion -- 1 Introduction -- 2 Related Work -- 2.1 SISR -- 2.2 VSR -- 3 Methodology -- 3.1 Overview -- 3.2 Internal-Data Based VSR -- 3.3 Spatio-Temporal VSR -- 4 Experiments -- 4.1 Protocols -- 4.2 Real-World Testing -- 4.3 Ablation Study -- 5 Conclusion -- References -- ODE-Inspired Image Denoiser: An End-to-End Dynamical Denoising Network -- 1 Introduction -- 2 Related Work -- 2.1 Image Denoising with CNN -- 2.2 Neural ODEs V.S. Residual Learning -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Problem Formulation -- 3.3 OI-Block -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Synthetic Noisy Images -- 4.3 Real Noisy Images -- 5 Conclusion -- References -- Image Outpainting with Depth Assistance -- 1 Introduction -- 2 Related Work -- 3 Our Model -- 3.1 Framework Design -- 3.2 Training -- 4 Experiment -- 4.1 Dataset -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Comparison of Depth Feature Extraction Solutions -- 5 Conclusion -- References -- Light-Weight Multi-channel Aggregation Network for Image Super-Resolution -- 1 Introduction.
2 Proposed Method.
Record Nr. UNINA-9910506383503321
Cham, Switzerland : , : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pattern recognition and computer vision . Part III : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pattern recognition and computer vision . Part III : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , 2022
Descrizione fisica 1 online resource (648 pages)
Disciplina 006.4
Collana Lecture notes in computer science
Soggetto topico Pattern recognition systems
Computer vision
ISBN 3-030-88010-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part III -- Low-Level Vision and Image Processing -- SaliencyBERT: Recurrent Attention Network for Target-Oriented Multimodal Sentiment Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Task Definition -- 3.2 Recurrent Attention Network -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results and Analysis -- 5 Conclusion -- References -- Latency-Constrained Spatial-Temporal Aggregated Architecture Search for Video Deraining -- 1 Introduction -- 2 The Proposed Method -- 2.1 Spatial-Temporal Aggregated Architecture -- 2.2 Architecture Search -- 3 Experimental Results -- 3.1 Experiment Preparation -- 3.2 Running Time Evaluation -- 3.3 Quantitative Comparison -- 3.4 Qualitative Comparison -- 3.5 Ablation Study -- 4 Conclusions -- References -- Semantic-Driven Context Aggregation Network for Underwater Image Enhancement -- 1 Introduction -- 2 Method -- 2.1 The Overall Architecture -- 2.2 Semantic Feature Extractor -- 2.3 Multi-scale Feature Transformation Module -- 2.4 Context Aggregation Enhancement Network and Loss Function -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Comparison with the State-of-the-Arts -- 3.3 Ablation Study -- 3.4 Application on Salient Object Detection -- 4 Conclusion -- References -- A Multi-resolution Medical Image Fusion Network with Iterative Back-Projection -- 1 Introduction -- 2 Proposed Approach -- 2.1 Overall Framework -- 2.2 Network Architecture -- 2.3 Loss Function -- 3 Experiments -- 3.1 Dataset and Training Details -- 3.2 Results and Analysis of IBPNet -- 4 Conclusions -- References -- Multi-level Discriminator and Wavelet Loss for Image Inpainting with Large Missing Area -- 1 Introduction -- 2 Related Work -- 2.1 Image Inpainting -- 2.2 Adversarial Training -- 3 Our Approach -- 3.1 Multi-level Discriminator -- 3.2 Wavelet Loss.
4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 5 Conclusion -- References -- 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement -- 1 Introduction -- 2 Related Work -- 2.1 Low-Light Image Enhancement -- 2.2 Low-Light Video Enhancement -- 3 Method -- 3.1 Problem Formulation -- 3.2 Overview of the Pipeline -- 3.3 RSTAB Module -- 3.4 Unet Architecture with Global Projection -- 4 Experiment -- 4.1 Experimental Setting -- 4.2 Comparison with State-of-the-art Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- Single Image Specular Highlight Removal on Natural Scenes -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Scene Illumination Evaluation -- 3.2 Smooth Feature Extraction -- 3.3 Coefficient Estimation and Highlight Removal -- 4 Experiments -- 4.1 Quantitative Comparison on Laboratory Images -- 4.2 Visual Effect Comparison on Natural Scene Images -- 4.3 Discussion of Important Parameters -- 5 Conclusion -- References -- Document Image Binarization Using Visibility Detection and Point Cloud Segmentation -- 1 Introduction -- 2 TPO(Target-Point Occlusion) -- 2.1 Point Cloud Transformation -- 2.2 Convex Hull -- 3 Algorithm -- 3.1 Binarization -- 3.2 Unshadow Binarization -- 4 Experiment -- 5 Conclusion -- References -- LF-MAGNet: Learning Mutual Attention Guidance of Sub-Aperture Images for Light Field Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Light Field Image Super-Resolution -- 2.2 Visual Attention Mechanism -- 3 Proposed Method -- 3.1 Shallow Feature Extraction -- 3.2 Mutual Attention Guidance -- 3.3 LF Image Reconstruction -- 4 Experiment -- 4.1 Dataset and Implementation Details -- 4.2 Ablation Studies -- 4.3 Comparisons with the State-of-The-Arts -- 5 Conclusion -- References.
Infrared Small Target Detection Based on Weighted Variation Coefficient Local Contrast Measure -- 1 Introduction -- 2 The Proposed Algorithm -- 2.1 Variation Coefficient Local Contrast Measure -- 2.2 Weighted Variation Coefficient Local Contrast Measure -- 2.3 Target Detection -- 3 Experimental Results -- 3.1 Enhancement Performance Comparison -- 3.2 Detection Performance Comparison -- 4 Conclusion -- References -- Scale-Aware Distillation Network for Lightweight Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Scale-Aware Distillation Block -- 3.3 Comparisons with Other Information Distillation Methods -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with the State-of-the-Arts -- 5 Conclusion -- References -- Deep Multi-Illumination Fusion for Low-Light Image Enhancement -- 1 Introduction -- 2 Deep Multi-Illumination Fusion -- 2.1 Network Structure -- 2.2 Loss Function -- 3 Experimental Results -- 3.1 Implementation Details -- 3.2 Performance Evaluation -- 3.3 Ablation Analysis -- 3.4 Object Instance Segmentation -- 4 Conclusion -- References -- Relational Attention with Textual Enhanced Transformer for Image Captioning -- 1 Introduction -- 2 Related Work -- 2.1 Relationship Exploration -- 2.2 Transformer Architecture -- 3 The Proposed Approach -- 3.1 Relation Module -- 3.2 Attention Module -- 3.3 Decoder Module -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with State-of-the-Art -- 5 Conclusion -- References -- Non-local Network Routing for Perceptual Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Non-local Network Routing -- 3.2 Model Learning -- 4 Experiments -- 4.1 Evaluation Dataset and Metric.
4.2 Implementation Details -- 4.3 Derived Architecture -- 4.4 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Multi-focus Image Fusion with Cooperative Image Multiscale Decomposition -- 1 Introduction -- 2 Cooperative Image Multiscale Decomposition Based MGF -- 2.1 Mutually Guided Filter -- 2.2 Cooperative Image Multiscale Decomposition -- 3 Image Fusion with CIMD -- 3.1 Base Layers Fusion -- 3.2 Detailed Layers Fusion -- 3.3 Reconstruction -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Comparison to Classical Fusion Method -- 5 Conclusions -- References -- An Enhanced Multi-frequency Learned Image Compression Method -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Formulation of Multi-frequency Learned Compression Models -- 3.2 Channel Attention Scheme -- 3.3 Decoder-Side Enhancement -- 4 Experiment Results -- 4.1 Parameter Description -- 4.2 Results Evaluation -- 5 Conclusion -- References -- Noise Map Guided Inpainting Network for Low-Light Image Enhancement -- 1 Introduction -- 2 Related Works -- 2.1 Low-Light Image Enhancement -- 2.2 Image Inpainting -- 3 Method -- 3.1 Stage I: Decomposition -- 3.2 Stage II: Restoration -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusion -- References -- FIE-GAN: Illumination Enhancement Network for Face Recognition -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Architecture -- 3.2 Loss Function -- 3.3 Deployment in Face Recognition -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Visual Perception Results -- 4.3 Face Recognition Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Illumination-Aware Image Quality Assessment for Enhanced Low-Light Image -- 1 Introduction -- 2 Illumination-Aware Quality Assessment of Enhanced Low-Light Image -- 2.1 Intrinsic Decomposition Module.
2.2 CNN-based Feature Extraction Module -- 2.3 Learnable Perceptual Distance Measurement -- 3 Experiments -- 3.1 Basic Evaluations -- 3.2 Evaluations of LIE-IQA with Related Methods -- 3.3 LIE-IQA for Low-Light Enhancement -- 4 Conclusion -- References -- Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution -- 1 Introduction -- 2 Tensor Notations and Preliminaries -- 3 Problem Formulation -- 3.1 Problem Description and Degradation Model -- 3.2 MAP Formulation -- 3.3 Smooth Coupled Tucker Decomposition Model -- 3.4 Optimization -- 4 Experimental Results -- 4.1 Experimental Settings and Implementation Issues -- 4.2 Experimental Results -- 4.3 Choice of Model Order -- 5 Conclusion -- 1. References -- Self-Supervised Video Super-Resolution by Spatial Constraint and Temporal Fusion -- 1 Introduction -- 2 Related Work -- 2.1 SISR -- 2.2 VSR -- 3 Methodology -- 3.1 Overview -- 3.2 Internal-Data Based VSR -- 3.3 Spatio-Temporal VSR -- 4 Experiments -- 4.1 Protocols -- 4.2 Real-World Testing -- 4.3 Ablation Study -- 5 Conclusion -- References -- ODE-Inspired Image Denoiser: An End-to-End Dynamical Denoising Network -- 1 Introduction -- 2 Related Work -- 2.1 Image Denoising with CNN -- 2.2 Neural ODEs V.S. Residual Learning -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Problem Formulation -- 3.3 OI-Block -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Synthetic Noisy Images -- 4.3 Real Noisy Images -- 5 Conclusion -- References -- Image Outpainting with Depth Assistance -- 1 Introduction -- 2 Related Work -- 3 Our Model -- 3.1 Framework Design -- 3.2 Training -- 4 Experiment -- 4.1 Dataset -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Comparison of Depth Feature Extraction Solutions -- 5 Conclusion -- References -- Light-Weight Multi-channel Aggregation Network for Image Super-Resolution -- 1 Introduction.
2 Proposed Method.
Record Nr. UNISA-996464422803316
Cham, Switzerland : , : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Pattern recognition and computer vision . Part II : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pattern recognition and computer vision . Part II : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (694 pages)
Disciplina 006.4
Collana Lecture notes in computer science
Soggetto topico Pattern recognition systems
Computer vision
ISBN 3-030-88007-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Computer Vision, Theories and Applications -- Dynamic Fusion Network for Light Field Depth Estimation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 The Overall Architecture -- 3.2 Pyramid ConvGRU -- 3.3 Multi-modal Dynamic Fusion Module (MDFM) -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-arts -- 5 Conclusion -- References -- Metric Calibration of Aerial On-Board Multiple Non-overlapping Cameras Based on Visual and Inertial Measurement Data -- 1 Introduction -- 2 Related Works -- 3 Metric Calibration Based on Visual and Inertial Measurement Data -- 3.1 Notation and Problem Formulation -- 3.2 Relative Pose Estimation via Structure from Motion -- 3.3 Inertial Measurement Data Based Metric Scale Factor Estimation -- 4 Experimental Results -- 4.1 Equipment -- 4.2 Metric Calibration of the Aerial On-Board Non-overlapping Camera System -- 4.3 Metric Calibration of an Industrial Non-overlapping Camera System -- 4.4 Experiments of Applications for Object Metric 3D Reconstruction -- 5 Conclusions -- References -- SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Cross Interaction Pattern -- 3.2 Aggregate Interaction Module -- 3.3 Bidirectional Fusion Module -- 3.4 Training Loss -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Evaluation Metrics -- 4.3 Ablation Studies -- 4.4 Robustness Analysis -- 4.5 Comparing with State-of-the-Art -- 5 Conclusion -- References -- Video-Based Reconstruction of Smooth 3D Human Body Motion -- 1 Introduction -- 2 Related Work -- 2.1 3D Human Mesh from Single Images -- 2.2 3D Human Mesh from Video -- 2.3 GANs for Modeling -- 3 Approach -- 3.1 3D Body Representation -- 3.2 Temporal Encoder.
3.3 Constraint Loss -- 3.4 Motion Discriminator -- 4 Experiments -- 4.1 Implement Details -- 4.2 Comparison to Other Methods -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- A Unified Modular Framework with Deep Graph Convolutional Networks for Multi-label Image Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Image Feature Extraction Module -- 3.2 Label Semantic Extraction Module -- 3.3 Prediction Results and Training Scheme -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Ablation Studies -- 4.5 Adjacency Matrix Visualization -- 5 Conclusion -- References -- 3D Correspondence Grouping with Compatibility Features -- 1 Introduction -- 2 Related Work -- 2.1 3D Correspondence Grouping -- 2.2 Learning for Correspondence Grouping -- 3 Methodology -- 3.1 Compatibility Check -- 3.2 CF Feature Extraction -- 3.3 CF Classification -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Method Analysis -- 4.3 Comparative Results and Visualization -- 5 Conclusions -- References -- Contour-Aware Panoptic Segmentation Network -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Panoptic Contour Branch -- 3.2 Panoptic Segmentation Branch -- 3.3 Structure Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Comparisons with Other Methods -- 4.5 Ablative Analysis -- 5 Conclusion -- References -- VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder -- 1 Introduction -- 2 Realted Work -- 2.1 Handcraft-Based Methods -- 2.2 CNN-Based Methods -- 2.3 AE-Based Methods -- 3 VGG16-Based Convolutional Autoencoder -- 3.1 Model Architecture -- 3.2 Training -- 3.3 Matching -- 4 Experiments -- 4.1 Datasets -- 4.2 State-of-the-Art Approaches -- 4.3 Ground Truth -- 4.4 Comparison and Discussion.
5 Conclusion -- References -- Slice Sequential Network: A Lightweight Unsupervised Point Cloud Completion Network -- 1 Introduction -- 2 Related Work -- 2.1 3D Learning -- 2.2 3D Completion -- 3 Our Method -- 3.1 Overview -- 3.2 Slicer -- 3.3 Multi-scale Point Encoder -- 3.4 Sequential Predictor -- 3.5 Shape Prediction Decoder -- 3.6 Loss Function -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Point Cloud Completion Results -- 4.3 Analysis of Encoder -- 4.4 Robustness to Occlusion -- 4.5 Comparison of Complexity -- 5 Ablation Study -- 6 Conclusion -- References -- From Digital Model to Reality Application: A Domain Adaptation Method for Rail Defect Detection -- 1 Introduction -- 2 Preliminaries -- 3 Method -- 3.1 DT-Based Virtual Data Generation -- 3.2 Dummy-Target Domain -- 3.3 DA-YOLO -- 4 Experiment -- 4.1 Dataset and Evaluation Metrics -- 4.2 Experiment Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- FMixAugment for Semi-supervised Learning with Consistency Regularization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 FMixAugment: MixAugment Combined with FMask -- 3.2 Improved Consistency Regularization -- 3.3 Dynamic Growth Threshold -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion and Future Work -- References -- IDANet: Iterative D-LinkNets with Attention for Road Extraction from High-Resolution Satellite Imagery -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Basic Iteration Module -- 3.3 Iterative Architecture -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 5 Results -- 5.1 Comparison of Road Segmentation Methods -- 5.2 Ablation Experiment -- 5.3 The Influence of Network Iteration -- 6 Conclusion -- References.
Disentangling Deep Network for Reconstructing 3D Object Shapes from Single 2D Images -- 1 Introduction -- 2 Related Works -- 3 Disentangling Deep Network -- 3.1 Network Architecture -- 3.2 Learning Objective Functions -- 3.3 Training Strategy -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Analysis -- 4.3 3D Reconstruction -- 4.4 Effects of 3D Shape Identity -- 5 Conclusion -- References -- AnchorConv: Anchor Convolution for Point Clouds Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 AnchorConv -- 3.2 Anchor Reweighting Module -- 3.3 Network Architectures -- 4 Experiments -- 4.1 Classification on ModelNet40 -- 4.2 ShapeNet Part Segmentation -- 4.3 3D Segmentation of Indoor Scene -- 4.4 3D Segmentation of Outdoor Scene -- 4.5 Ablation Study -- 4.6 Qualitative Results -- 5 Conclusion -- References -- IFR: Iterative Fusion Based Recognizer for Low Quality Scene Text Recognition -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Iterative Collaboration -- 3.2 Fusion Module RRF -- 3.3 Loss Functions -- 3.4 Paired Training Data Generate -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with State-of-the-Arts -- 5 Conclusion -- References -- Immersive Traditional Chinese Portrait Painting: Research on Style Transfer and Face Replacement -- 1 Introduction -- 2 Related Work -- 2.1 Neural Style Transfer -- 2.2 Face Replacement -- 3 The P-CP Method -- 3.1 Network Architecture -- 3.2 Neural Style Transfer Network -- 3.3 Face Replacement -- 4 Experiment -- 4.1 Comparison of Different Traditional Chinese Painting Styles -- 4.2 Image Detail Exploration and Optimization -- 4.3 Improvement of Face Replacement with Style Transfer -- 5 Conclusion -- References -- Multi-camera Extrinsic Auto-calibration Using Pedestrians in Occluded Environments -- 1 Introduction.
2 Related Work -- 3 Calibration Based on 3D Positions -- 3.1 3D Head Positions in Local Camera Coordinates -- 3.2 Registration of 3D Point Sets -- 4 Refinement -- 5 Experiments and Results -- 6 Conclusion -- References -- Dual-Layer Barcodes -- 1 Introduction -- 2 Related Work -- 2.1 Steganography -- 2.2 Watermarking -- 2.3 Barcode -- 3 Method -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Noise Layer -- 3.4 Discriminator -- 4 Experiments and Analysis -- 4.1 Dataset and Experimental Setting -- 4.2 Implementation Details -- 4.3 Metrics -- 5 Discussion -- 6 Conclusion -- References -- Graph Matching Based Robust Line Segment Correspondence for Active Camera Relocalization -- 1 Introduction -- 2 Method -- 2.1 System Overview -- 2.2 Robust Line Segment Matching -- 2.3 Active Camera Relocation -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Analysis of Line Segment Matching -- 3.3 Analysis of Relocalization Accuracy and Convergence Speed -- 3.4 Analysis of Robustness in Hard Scenes -- 4 Conclusion -- References -- Unsupervised Learning Framework for 3D Reconstruction from Face Sketch -- 1 Introduction -- 2 Related Work -- 2.1 Image-to-Image Translation -- 2.2 3D Shape Reconstruction -- 3 Method -- 3.1 Dataset Construction -- 3.2 Network Architecture -- 3.3 Loss Functions -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Quantitative Results and Ablation Study -- 4.3 Qualitative Results -- 5 Conclusion -- References -- HEI-Human: A Hybrid Explicit and Implicit Method for Single-View 3D Clothed Human Reconstruction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Explicit Model -- 3.3 Implicit Model -- 3.4 Loss Functions -- 4 Experiments -- 4.1 Dataset and Protocol -- 4.2 Training Details -- 4.3 Quantitative Results -- 4.4 Qualitative Results -- 4.5 Ablation Studies -- 5 Conclusions -- References.
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction.
Record Nr. UNINA-9910506384603321
Cham, Switzerland : , : Springer, , [2021]
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Pattern recognition and computer vision : 4th Chinese conference, PRCV 2021, Beijing, China, October 29 - November 1, 2021, proceedings, part I / / Huimin Ma [and seven others], editors
Pattern recognition and computer vision : 4th Chinese conference, PRCV 2021, Beijing, China, October 29 - November 1, 2021, proceedings, part I / / Huimin Ma [and seven others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (634 pages)
Disciplina 621.367
Collana Lecture Notes in Computer Science
Soggetto topico Optical data processing
Artificial intelligence
Computer vision
ISBN 3-030-88004-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Object Detection, Tracking and Recognition -- High-Performance Discriminative Tracking with Target-Aware Feature Embeddings -- 1 Introduction -- 2 Discriminative Tracking with Target-Aware Feature Embeddings -- 2.1 Target-Unaware Feature Extraction -- 2.2 Target-Aware Feature Construction -- 2.3 Ridge Regression with Target-Aware Feature Embeddings -- 2.4 Offline Training -- 2.5 Online Tracking -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Feature Comparisons -- 3.3 State-of-the-Art Comparisons -- 4 Conclusion -- References -- 3D Multi-object Detection and Tracking with Sparse Stationary LiDAR -- 1 Introduction -- 2 Related Work -- 2.1 3D Object Detection -- 2.2 3D Multi-Object Tracking -- 3 Proposed Method -- 3.1 Tracklet Regression -- 3.2 Data Association -- 3.3 Football Game Dataset -- 4 Experiments -- 4.1 Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- CRNet: Centroid Radiation Network for Temporal Action Localization -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Notation and Preliminaries -- 3.2 Feature Extractor Network -- 3.3 Relation Network -- 3.4 Centroids Prediction -- 3.5 Instance Generation -- 3.6 Overall Objective Before Random Walk -- 3.7 Prediction and Post-processing -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation of RelNet, CenNet and OffNet -- 4.4 Performance with Fewer Data -- 4.5 Comparisons with State-of-the-Art -- 5 Conclusion -- References -- Weakly Supervised Temporal Action Localization with Segment-Level Labels -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Problem Statement and Notation -- 3.2 Architecture -- 3.3 Classification Loss -- 3.4 Partial Segment Loss -- 3.5 Sphere Loss -- 3.6 Propagation Loss -- 3.7 Classification and Localization -- 4 Experiments.
4.1 Experimental Setup -- 4.2 Exploratory Experiments -- 4.3 Comparisons with the State-of-the-art -- 5 Conclusion -- References -- Locality-Constrained Collaborative Representation with Multi-resolution Dictionary for Face Recognition -- 1 Introduction -- 2 Proposed Method -- 2.1 Notations -- 2.2 Model of LCCR-MRD -- 2.3 Optimization -- 2.4 Classification -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Results and Discussions -- 4 Conclusion -- References -- Fast and Fusion: Real-Time Pedestrian Detector Boosted by Body-Head Fusion -- 1 Introdution -- 2 Related Work -- 3 Fast and Fusion -- 3.1 Baseline -- 3.2 Body-Head Fusion -- 3.3 Auxiliary Training Task -- 4 Experiment -- 4.1 Datasets and Evaluation Metric -- 4.2 Evaluation on Extended CityPersons Dataset -- 4.3 Evaluation on CrowdHuman Dataset -- 4.4 Ablation Study -- 5 Conclusion -- References -- STA-GCN: Spatio-Temporal AU Graph Convolution Network for Facial Micro-expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Micro-Expression Recognition -- 2.2 Graph Convolution Network -- 3 Method -- 3.1 ROI Division -- 3.2 3D CNN with Non-Local Block -- 3.3 AU-attention Graph Convolution -- 3.4 Loss Function -- 4 Experiment -- 4.1 Experimental Setting -- 4.2 Implementation Details -- 4.3 Experimental Result -- 5 Conclusion -- References -- Attentive Contrast Learning Network for Fine-Grained Classification -- 1 Introduction -- 2 Method -- 2.1 Attention Generator -- 2.2 Contrastive Learning Module -- 2.3 Synergic Learning Module -- 2.4 Learning Attentive Contrast Learning Networks -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparison with Other Methods -- 3.5 Visualization Results -- 4 Conclusion -- References -- Relation-Based Knowledge Distillation for Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 CAE-Based Methods.
2.2 GAN-Based Methods -- 2.3 KD-Based Methods -- 3 Method -- 3.1 Gram Matrix and the "FSP Matrix" -- 3.2 The Proposed Approach -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Datasets -- 4.3 Results -- 5 Conclusion -- References -- High Power-Efficient and Performance-Density FPGA Accelerator for CNN-Based Object Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 System Framework -- 3.2 Neural Network Accelerator -- 4 Experiments -- 5 Conclusion -- References -- Relation-Guided Actor Attention for Group Activity Recognition -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Location-Aware Relation Module -- 3.2 Relation-Guided Actor Attention Module -- 3.3 Classification Layer -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Comparison with the State-of-the-Arts -- 5 Conclusion -- References -- MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification -- 1 Introduction -- 2 Related Works -- 2.1 Representation Learning and Metric Learning in ReID -- 2.2 View-Based Methods for ReID -- 2.3 Domain Adaptation -- 3 The Proposed Method -- 3.1 Baggage ReID Baseline -- 3.2 Multi-view Attention Model -- 3.3 Domain-Invariant Learning -- 4 Experiments -- 4.1 Dataset and Protocols -- 4.2 Implementation Details -- 4.3 Effectiveness of Multi-view Attention -- 4.4 Effectiveness of Domain-Invariant Learning -- 4.5 Comparison with Other Methods -- 5 Conclusion -- References -- Joint Attention Mechanism for Unsupervised Video Object Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Joint Attention Mechanism -- 3.2 Network Architecture -- 4 Experiments -- 4.1 Datasets and Evaluation -- 4.2 Ablation Study -- 4.3 Comparison to the State-Of-The-Arts -- 5 Conclusion -- References -- Foreground Feature Selection and Alignment for Adaptive Object Detection.
1 Introduction -- 2 Related Work -- 2.1 Object Detection -- 2.2 Adaptive Object Detection -- 3 Method -- 3.1 Framework Overview -- 3.2 Foreground Selection Module -- 3.3 Multi-level Domain Adaptation -- 3.4 Overall Objective -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Adaptation Results -- 4.3 Visualization and Discussion -- 5 Conclusions -- References -- Exploring Category-Shared and Category-Specific Features for Fine-Grained Image Classification -- 1 Introduction -- 2 Proposed Method -- 2.1 Category-Shared Feature Extraction Module -- 2.2 Category-Specific Feature Extraction Module -- 3 Experiment -- 3.1 Implementation Details -- 3.2 Experimental Results -- 3.3 Ablation Studies -- 3.4 Visualizations -- 4 Conclusions -- References -- Deep Mixture of Adversarial Autoencoders Clustering Network -- 1 Introduction -- 2 Mixture of Adversarial Autoencoders -- 2.1 Adversarial Block -- 2.2 Target Distribution -- 2.3 Loss Function -- 2.4 Training Procedure -- 3 Experiment -- 3.1 Clustering Results -- 3.2 Reconstruct and Generate -- 4 Conclusion -- References -- SA-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Scale-Aware Feature Extractor -- 3.2 Density-Localization Interaction Module -- 3.3 Loss Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Comparison with State-of-the-Arts -- 4.5 Ablation Study -- 5 Conclusion -- References -- Conditioners for Adaptive Regression Tracking -- 1 Introduction -- 2 Related Work -- 2.1 One-Stage Visual Tracking -- 2.2 Conditional Instance Learning -- 3 The Proposed Conditional Regression Tracking -- 3.1 Conditional Batch Normalization -- 3.2 Visual Context and Trajectory Formulating -- 3.3 Visual Context Network -- 3.4 Trajectory Network.
3.5 Implementation, Training and Inference -- 4 Experiments -- 4.1 Ablation Study -- 5 Conclusions -- References -- Attention Template Update Model for Siamese Tracker -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Traditional Update -- 3.2 Network Architecture -- 3.3 Adjustment and Update Blocks -- 3.4 Channel Attention Block -- 3.5 Training Model -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Checkpoint Selection -- 4.3 Performance in Several Benchmarks -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Insight on Attention Modules for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Skeleton-Based Action Recognition -- 2.2 Attention Mechanisms -- 3 Multi-category Attention Modules -- 3.1 Spatial-Wise Attention Module -- 3.2 Temporal-Wise Attention Module -- 3.3 Spatiotemporal Attention Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Ablation Studies -- 4.3 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- AO-AutoTrack: Anti-occlusion Real-Time UAV Tracking Based on Spatio-temporal Context -- 1 Introduction -- 2 Related Work -- 2.1 Discriminative Correlation Filter Tracking Algorithm -- 2.2 Anti-occlusion Object Tracking -- 2.3 DCF Onboard UAV -- 3 Proposed Tracking Approach -- 3.1 Review AutoTrack -- 3.2 Temporal Regularization Analysis and Improvement -- 3.3 Re-detection Mechanism -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Comparison with Hand-Crafted Based Trackers -- 4.3 Re-detection Evaluation -- 5 Conclusions -- References -- Two-Stage Recognition Algorithm for Untrimmed Converter Steelmaking Flame Video -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Feature Extraction -- 3.2 Recognition for Untrimmed Flame Videos -- 4 Experiments -- 4.1 Datasets -- 4.2 Implemented Details -- 4.3 Data Analysis -- 5 Conclusion -- References.
Scale-Aware Multi-branch Decoder for Salient Object Detection.
Record Nr. UNINA-9910506388303321
Cham, Switzerland : , : Springer, , [2021]
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Lo trovi qui: Univ. Federico II
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Pattern recognition and computer vision . Part IV : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pattern recognition and computer vision . Part IV : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (594 pages)
Disciplina 006.4
Collana Lecture notes in computer science
Soggetto topico Pattern recognition systems
Computer vision
ISBN 3-030-88013-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part IV -- Machine Learning, Neural Network and Deep Learning -- Edge-Wise One-Level Global Pruning on NAS Generated Networks -- 1 Introduction -- 2 Related Work -- 2.1 Neural Architecture Search -- 2.2 Network Pruning -- 3 Edge-Wise One-Level Global Pruning on DARTS-Based Network -- 3.1 Edge Weight Assignment -- 3.2 One-Level Structure Learning -- 3.3 Procedure of EOG-Pruning -- 3.4 Comparison with Previous Work -- 4 Experiments -- 4.1 Datasets -- 4.2 EOG-Pruning on CIFAR-10 and CIFAR-100 -- 4.3 EOG-Pruning on ImageNet -- 4.4 Visualization -- 4.5 Ablation Study -- 5 Conclusion -- References -- Convolution Tells Where to Look -- 1 Introduction -- 2 Related Work -- 3 Feature Difference Module -- 3.1 Feature Difference -- 3.2 FD Networks -- 4 Experiments -- 4.1 Results on CIFAR-10 and CIFAR-100 -- 4.2 ImageNet Classification -- 4.3 VOC 2012 Object Detection -- 5 Analysis and Interpretation -- 6 Conclusion -- References -- Robust Single-Step Adversarial Training with Regularizer -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Training -- 2.2 Single-Step Adversarial Training -- 3 Proposed Approach -- 3.1 Observation -- 3.2 PGD Regularization -- 3.3 Training Route -- 4 Experiments -- 4.1 Results on MNIST -- 4.2 Results on CIFAR-10 -- 5 Conclusion -- References -- Texture-Guided U-Net for OCT-to-OCTA Generation -- 1 Introduction -- 2 Method -- 3 Experiments -- 3.1 Dataset and Metrics -- 3.2 Results -- 4 Conclusion -- References -- Learning Key Actors and Their Interactions for Group Activity Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Group Activity Recognition -- 2.2 Graph Neural Networks -- 3 Approach and Framework -- 3.1 Preliminaries -- 3.2 Extracting the SARG -- 3.3 Feature Fusion -- 3.4 Training Loss -- 4 Experiments -- 4.1 Ablation Studies -- 4.2 Compared with SOTA -- 5 Conclusion.
References -- Attributed Non-negative Matrix Multi-factorization for Data Representation -- 1 Introduction -- 2 Related Work -- 2.1 NMF -- 2.2 GNMF -- 3 The Proposed Method -- 3.1 Motivation and Objective Function -- 3.2 Model Optimization -- 3.3 Algorithm Analysis -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Experiment Results -- 4.3 Parameter Analysis -- 4.4 Convergence Study -- 5 Conclusions -- References -- Improved Categorical Cross-Entropy Loss for Training Deep Neural Networks with Noisy Labels -- 1 Introduction -- 2 Improved Categorical Cross-Entropy Loss for Noise-Robust Classification -- 2.1 Robustness Analysis of CCE and MAE -- 2.2 Improved Categorical Cross Entropy -- 2.3 Theoretical Analysis of ICCE -- 3 Experiment -- 3.1 Dataset and Model Architectures -- 3.2 Label Noise -- 3.3 Evaluation of the CIFAR Dataset with Synthetic Noise -- 4 Conclusion -- References -- A Residual Correction Approach for Semi-supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Supervised Semantic Segmentation -- 2.2 Semi-supervised Semantic Segmentation -- 3 Method -- 3.1 Residual Correction Network -- 3.2 Supervised Training -- 3.3 Semi-supervised Training -- 4 Experiments -- 4.1 Network Architecture -- 4.2 Datasets and Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Results -- 5 Conclusions -- References -- Hypergraph Convolutional Network with Hybrid Higher-Order Neighbors -- 1 Introduction -- 2 Related Works -- 2.1 Graph Neural Networks -- 2.2 Learning on Hypergraph -- 2.3 Hypergraph Neural Network -- 3 Preliminary Knowledge -- 3.1 Hypergraph -- 3.2 Hypergraph Convolution Network -- 4 Method -- 5 Experiments -- 5.1 Datasets and Baseline -- 5.2 Experimental Setting -- 5.3 Experimental Results and Discussion -- 6 Conclusions -- References -- Text-Aware Single Image Specular Highlight Removal -- 1 Introduction.
2 Related Work -- 2.1 Dichromatic Reflection Model-Based Methods -- 2.2 Inpainting-Based Methods -- 2.3 Deep Learning-Based Methods -- 3 Datasets -- 3.1 Real Dataset -- 3.2 Synthetic Datasets -- 4 Proposed Method -- 4.1 Network Architecture -- 4.2 Loss Functions -- 5 Experiments -- 5.1 Implementation Settings -- 5.2 Qualitative Evaluation -- 5.3 Quantitative Evaluation -- 5.4 Ablation Study -- 6 Conclusion and Future Work -- References -- Minimizing Wasserstein-1 Distance by Quantile Regression for GANs Model -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Wasserstein GAN -- 3.2 Quantile Regression -- 4 Our Method -- 5 Experiment -- 5.1 Basic Setting -- 5.2 IS and FID Criteria Experiment -- 5.3 Adam and Rmsprob Experiment -- 5.4 Different Quantile Initialization Experiment -- 6 Conclusion -- References -- A Competition of Shape and Texture Bias by Multi-view Image Representation -- 1 Introduction -- 2 Multi-view Image Representations -- 2.1 Background Representations -- 2.2 Shape Representations -- 2.3 Texture Representations -- 3 Loss -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results and Analysis -- 5 Conclusion and Future Work -- References -- Learning Indistinguishable and Transferable Adversarial Examples -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Example Generation -- 2.2 The Fast Gradient Sign Method Series -- 3 Methodology -- 3.1 The Piecewise Linear Function -- 3.2 The Gradient Regularization Term -- 3.3 The Mixed-Input Strategy -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Ablation Study -- 4.3 The Results of Single-Model Attacks -- 4.4 The Results of Multi-model Attacks -- 5 Conclusion -- References -- Efficient Object Detection and Classification of Ground Objects from Thermal Infrared Remote Sensing Image Based on Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology.
3.1 Backbone Network -- 3.2 Feature Pyramid Networks -- 3.3 Adaptive Multiscale Receptive Field -- 3.4 Loss Function -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Results -- 5 Conclusions -- References -- MEMA-NAS: Memory-Efficient Multi-Agent Neural Architecture Search -- 1 Introduction -- 2 MEMA-NAS Framework -- 2.1 Multi-Agent Search Algorithm -- 2.2 Resource Constraint -- 3 Experiments -- 3.1 Datasets and Evaluations -- 3.2 Illustration of Searched Architecture and Analysis -- 3.3 Ablation Experiments for Individual Module Search -- 3.4 Generalization Performance Evaluation -- 3.5 Comparison with NAS-Based Detection Works -- 3.6 GPU Memory Consumption Evaluation -- 4 Conclusion -- References -- Adversarial Decoupling for Weakly Supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Initial Prediction for WSSS -- 2.2 Response Refinement for WSSS -- 3 Method -- 3.1 Sub-category Clustering -- 3.2 Context Decoupling Augmentation -- 3.3 Adversarial Climbing -- 4 Experiments -- 4.1 Evaluated Dataset and Metric -- 4.2 Ablation Study and Analysis -- 4.3 Semantic Segmentation Performance -- 5 Conclusion -- References -- Towards End-to-End Embroidery Style Generation: A Paired Dataset and Benchmark -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Stitching Generation -- 2.2 Style Transfer -- 2.3 Image-to-Image Translation -- 2.4 Related Datasets -- 3 Paired Embroidery Dataset Overview -- 4 Automatic Embroidery Generation -- 4.1 Local Texture Generation -- 4.2 Global Fine-Tuning -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Evaluation Metrics -- 5.3 Experimental Results -- 6 Conclusion -- References -- Efficient and Real-Time Particle Detection via Encoder-Decoder Network -- 1 Introduction -- 2 Related Work -- 2.1 Particle Detection Methods -- 2.2 Decoder Architectures -- 2.3 Light-Weight Networks.
2.4 Knowledge Distillation -- 3 The Proposed Method -- 3.1 Network Framework -- 3.2 Loss Function -- 3.3 Structured Knowledge Distillation -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Evaluation Metrics -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Flexible Projection Search Using Optimal Re-weighted Adjacency for Unsupervised Manifold Learning -- 1 Introduction -- 2 Graph Construction and Metric Learning -- 2.1 Graph-Based Affinity Matrix -- 2.2 Graph Embedding -- 2.3 Distance Metric Learning -- 3 Flexible Projection Search Using Optimal Re-weighted Adjacency (FPSORA) -- 3.1 Quadratic Programming for Updating Adjacency Weights -- 3.2 Flexible Projection Search for Preservation of Original Locality -- 3.3 Complete Workflow of the Proposed FPSORA -- 4 Experiments and Analysis -- 4.1 Evaluation Metrics -- 4.2 Experimental Setup -- 4.3 Experiments and Analysis -- 5 Conclusion -- References -- Fabric Defect Detection via Multi-scale Feature Fusion-Based Saliency -- 1 Introduction -- 2 Proposed Method -- 2.1 Multi-scale Feature Learning Module -- 2.2 Feedback Attention Refinement Fusion Module -- 2.3 The Joint Loss -- 3 Experiments -- 3.1 Dataset and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with State-of-the-Arts -- 3.4 Ablation Study -- 4 Conclusion -- References -- Improving Adversarial Robustness of Detector via Objectness Regularization -- 1 Introduction -- 2 Method -- 2.1 Vanishing Adversarial Patch -- 2.2 Objectness Regularization -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Experimental Result -- 4 Conclusion -- References -- IPE Transformer for Depth Completion with Input-Aware Positional Embeddings -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Input-Aware Positional Embedding -- 3.2 IPE Transformer -- 3.3 Network Structure -- 4 Experiment -- 4.1 Setup.
4.2 Experiment Results.
Record Nr. UNINA-9910506409003321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Pattern recognition and computer vision : 4th Chinese conference, PRCV 2021, Beijing, China, October 29 - November 1, 2021, proceedings, part I / / Huimin Ma [and seven others], editors
Pattern recognition and computer vision : 4th Chinese conference, PRCV 2021, Beijing, China, October 29 - November 1, 2021, proceedings, part I / / Huimin Ma [and seven others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (634 pages)
Disciplina 621.367
Collana Lecture Notes in Computer Science
Soggetto topico Optical data processing
Artificial intelligence
Computer vision
ISBN 3-030-88004-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Object Detection, Tracking and Recognition -- High-Performance Discriminative Tracking with Target-Aware Feature Embeddings -- 1 Introduction -- 2 Discriminative Tracking with Target-Aware Feature Embeddings -- 2.1 Target-Unaware Feature Extraction -- 2.2 Target-Aware Feature Construction -- 2.3 Ridge Regression with Target-Aware Feature Embeddings -- 2.4 Offline Training -- 2.5 Online Tracking -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Feature Comparisons -- 3.3 State-of-the-Art Comparisons -- 4 Conclusion -- References -- 3D Multi-object Detection and Tracking with Sparse Stationary LiDAR -- 1 Introduction -- 2 Related Work -- 2.1 3D Object Detection -- 2.2 3D Multi-Object Tracking -- 3 Proposed Method -- 3.1 Tracklet Regression -- 3.2 Data Association -- 3.3 Football Game Dataset -- 4 Experiments -- 4.1 Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- CRNet: Centroid Radiation Network for Temporal Action Localization -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Notation and Preliminaries -- 3.2 Feature Extractor Network -- 3.3 Relation Network -- 3.4 Centroids Prediction -- 3.5 Instance Generation -- 3.6 Overall Objective Before Random Walk -- 3.7 Prediction and Post-processing -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation of RelNet, CenNet and OffNet -- 4.4 Performance with Fewer Data -- 4.5 Comparisons with State-of-the-Art -- 5 Conclusion -- References -- Weakly Supervised Temporal Action Localization with Segment-Level Labels -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Problem Statement and Notation -- 3.2 Architecture -- 3.3 Classification Loss -- 3.4 Partial Segment Loss -- 3.5 Sphere Loss -- 3.6 Propagation Loss -- 3.7 Classification and Localization -- 4 Experiments.
4.1 Experimental Setup -- 4.2 Exploratory Experiments -- 4.3 Comparisons with the State-of-the-art -- 5 Conclusion -- References -- Locality-Constrained Collaborative Representation with Multi-resolution Dictionary for Face Recognition -- 1 Introduction -- 2 Proposed Method -- 2.1 Notations -- 2.2 Model of LCCR-MRD -- 2.3 Optimization -- 2.4 Classification -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Results and Discussions -- 4 Conclusion -- References -- Fast and Fusion: Real-Time Pedestrian Detector Boosted by Body-Head Fusion -- 1 Introdution -- 2 Related Work -- 3 Fast and Fusion -- 3.1 Baseline -- 3.2 Body-Head Fusion -- 3.3 Auxiliary Training Task -- 4 Experiment -- 4.1 Datasets and Evaluation Metric -- 4.2 Evaluation on Extended CityPersons Dataset -- 4.3 Evaluation on CrowdHuman Dataset -- 4.4 Ablation Study -- 5 Conclusion -- References -- STA-GCN: Spatio-Temporal AU Graph Convolution Network for Facial Micro-expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Micro-Expression Recognition -- 2.2 Graph Convolution Network -- 3 Method -- 3.1 ROI Division -- 3.2 3D CNN with Non-Local Block -- 3.3 AU-attention Graph Convolution -- 3.4 Loss Function -- 4 Experiment -- 4.1 Experimental Setting -- 4.2 Implementation Details -- 4.3 Experimental Result -- 5 Conclusion -- References -- Attentive Contrast Learning Network for Fine-Grained Classification -- 1 Introduction -- 2 Method -- 2.1 Attention Generator -- 2.2 Contrastive Learning Module -- 2.3 Synergic Learning Module -- 2.4 Learning Attentive Contrast Learning Networks -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparison with Other Methods -- 3.5 Visualization Results -- 4 Conclusion -- References -- Relation-Based Knowledge Distillation for Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 CAE-Based Methods.
2.2 GAN-Based Methods -- 2.3 KD-Based Methods -- 3 Method -- 3.1 Gram Matrix and the "FSP Matrix" -- 3.2 The Proposed Approach -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Datasets -- 4.3 Results -- 5 Conclusion -- References -- High Power-Efficient and Performance-Density FPGA Accelerator for CNN-Based Object Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 System Framework -- 3.2 Neural Network Accelerator -- 4 Experiments -- 5 Conclusion -- References -- Relation-Guided Actor Attention for Group Activity Recognition -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Location-Aware Relation Module -- 3.2 Relation-Guided Actor Attention Module -- 3.3 Classification Layer -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Comparison with the State-of-the-Arts -- 5 Conclusion -- References -- MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification -- 1 Introduction -- 2 Related Works -- 2.1 Representation Learning and Metric Learning in ReID -- 2.2 View-Based Methods for ReID -- 2.3 Domain Adaptation -- 3 The Proposed Method -- 3.1 Baggage ReID Baseline -- 3.2 Multi-view Attention Model -- 3.3 Domain-Invariant Learning -- 4 Experiments -- 4.1 Dataset and Protocols -- 4.2 Implementation Details -- 4.3 Effectiveness of Multi-view Attention -- 4.4 Effectiveness of Domain-Invariant Learning -- 4.5 Comparison with Other Methods -- 5 Conclusion -- References -- Joint Attention Mechanism for Unsupervised Video Object Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Joint Attention Mechanism -- 3.2 Network Architecture -- 4 Experiments -- 4.1 Datasets and Evaluation -- 4.2 Ablation Study -- 4.3 Comparison to the State-Of-The-Arts -- 5 Conclusion -- References -- Foreground Feature Selection and Alignment for Adaptive Object Detection.
1 Introduction -- 2 Related Work -- 2.1 Object Detection -- 2.2 Adaptive Object Detection -- 3 Method -- 3.1 Framework Overview -- 3.2 Foreground Selection Module -- 3.3 Multi-level Domain Adaptation -- 3.4 Overall Objective -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Adaptation Results -- 4.3 Visualization and Discussion -- 5 Conclusions -- References -- Exploring Category-Shared and Category-Specific Features for Fine-Grained Image Classification -- 1 Introduction -- 2 Proposed Method -- 2.1 Category-Shared Feature Extraction Module -- 2.2 Category-Specific Feature Extraction Module -- 3 Experiment -- 3.1 Implementation Details -- 3.2 Experimental Results -- 3.3 Ablation Studies -- 3.4 Visualizations -- 4 Conclusions -- References -- Deep Mixture of Adversarial Autoencoders Clustering Network -- 1 Introduction -- 2 Mixture of Adversarial Autoencoders -- 2.1 Adversarial Block -- 2.2 Target Distribution -- 2.3 Loss Function -- 2.4 Training Procedure -- 3 Experiment -- 3.1 Clustering Results -- 3.2 Reconstruct and Generate -- 4 Conclusion -- References -- SA-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Scale-Aware Feature Extractor -- 3.2 Density-Localization Interaction Module -- 3.3 Loss Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Comparison with State-of-the-Arts -- 4.5 Ablation Study -- 5 Conclusion -- References -- Conditioners for Adaptive Regression Tracking -- 1 Introduction -- 2 Related Work -- 2.1 One-Stage Visual Tracking -- 2.2 Conditional Instance Learning -- 3 The Proposed Conditional Regression Tracking -- 3.1 Conditional Batch Normalization -- 3.2 Visual Context and Trajectory Formulating -- 3.3 Visual Context Network -- 3.4 Trajectory Network.
3.5 Implementation, Training and Inference -- 4 Experiments -- 4.1 Ablation Study -- 5 Conclusions -- References -- Attention Template Update Model for Siamese Tracker -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Traditional Update -- 3.2 Network Architecture -- 3.3 Adjustment and Update Blocks -- 3.4 Channel Attention Block -- 3.5 Training Model -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Checkpoint Selection -- 4.3 Performance in Several Benchmarks -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Insight on Attention Modules for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Skeleton-Based Action Recognition -- 2.2 Attention Mechanisms -- 3 Multi-category Attention Modules -- 3.1 Spatial-Wise Attention Module -- 3.2 Temporal-Wise Attention Module -- 3.3 Spatiotemporal Attention Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Ablation Studies -- 4.3 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- AO-AutoTrack: Anti-occlusion Real-Time UAV Tracking Based on Spatio-temporal Context -- 1 Introduction -- 2 Related Work -- 2.1 Discriminative Correlation Filter Tracking Algorithm -- 2.2 Anti-occlusion Object Tracking -- 2.3 DCF Onboard UAV -- 3 Proposed Tracking Approach -- 3.1 Review AutoTrack -- 3.2 Temporal Regularization Analysis and Improvement -- 3.3 Re-detection Mechanism -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Comparison with Hand-Crafted Based Trackers -- 4.3 Re-detection Evaluation -- 5 Conclusions -- References -- Two-Stage Recognition Algorithm for Untrimmed Converter Steelmaking Flame Video -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Feature Extraction -- 3.2 Recognition for Untrimmed Flame Videos -- 4 Experiments -- 4.1 Datasets -- 4.2 Implemented Details -- 4.3 Data Analysis -- 5 Conclusion -- References.
Scale-Aware Multi-branch Decoder for Salient Object Detection.
Record Nr. UNISA-996464395903316
Cham, Switzerland : , : Springer, , [2021]
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Pattern recognition and computer vision . Part II : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pattern recognition and computer vision . Part II : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (694 pages)
Disciplina 006.4
Collana Lecture notes in computer science
Soggetto topico Pattern recognition systems
Computer vision
ISBN 3-030-88007-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Computer Vision, Theories and Applications -- Dynamic Fusion Network for Light Field Depth Estimation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 The Overall Architecture -- 3.2 Pyramid ConvGRU -- 3.3 Multi-modal Dynamic Fusion Module (MDFM) -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-arts -- 5 Conclusion -- References -- Metric Calibration of Aerial On-Board Multiple Non-overlapping Cameras Based on Visual and Inertial Measurement Data -- 1 Introduction -- 2 Related Works -- 3 Metric Calibration Based on Visual and Inertial Measurement Data -- 3.1 Notation and Problem Formulation -- 3.2 Relative Pose Estimation via Structure from Motion -- 3.3 Inertial Measurement Data Based Metric Scale Factor Estimation -- 4 Experimental Results -- 4.1 Equipment -- 4.2 Metric Calibration of the Aerial On-Board Non-overlapping Camera System -- 4.3 Metric Calibration of an Industrial Non-overlapping Camera System -- 4.4 Experiments of Applications for Object Metric 3D Reconstruction -- 5 Conclusions -- References -- SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Cross Interaction Pattern -- 3.2 Aggregate Interaction Module -- 3.3 Bidirectional Fusion Module -- 3.4 Training Loss -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Evaluation Metrics -- 4.3 Ablation Studies -- 4.4 Robustness Analysis -- 4.5 Comparing with State-of-the-Art -- 5 Conclusion -- References -- Video-Based Reconstruction of Smooth 3D Human Body Motion -- 1 Introduction -- 2 Related Work -- 2.1 3D Human Mesh from Single Images -- 2.2 3D Human Mesh from Video -- 2.3 GANs for Modeling -- 3 Approach -- 3.1 3D Body Representation -- 3.2 Temporal Encoder.
3.3 Constraint Loss -- 3.4 Motion Discriminator -- 4 Experiments -- 4.1 Implement Details -- 4.2 Comparison to Other Methods -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- A Unified Modular Framework with Deep Graph Convolutional Networks for Multi-label Image Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Image Feature Extraction Module -- 3.2 Label Semantic Extraction Module -- 3.3 Prediction Results and Training Scheme -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Ablation Studies -- 4.5 Adjacency Matrix Visualization -- 5 Conclusion -- References -- 3D Correspondence Grouping with Compatibility Features -- 1 Introduction -- 2 Related Work -- 2.1 3D Correspondence Grouping -- 2.2 Learning for Correspondence Grouping -- 3 Methodology -- 3.1 Compatibility Check -- 3.2 CF Feature Extraction -- 3.3 CF Classification -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Method Analysis -- 4.3 Comparative Results and Visualization -- 5 Conclusions -- References -- Contour-Aware Panoptic Segmentation Network -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Panoptic Contour Branch -- 3.2 Panoptic Segmentation Branch -- 3.3 Structure Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Comparisons with Other Methods -- 4.5 Ablative Analysis -- 5 Conclusion -- References -- VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder -- 1 Introduction -- 2 Realted Work -- 2.1 Handcraft-Based Methods -- 2.2 CNN-Based Methods -- 2.3 AE-Based Methods -- 3 VGG16-Based Convolutional Autoencoder -- 3.1 Model Architecture -- 3.2 Training -- 3.3 Matching -- 4 Experiments -- 4.1 Datasets -- 4.2 State-of-the-Art Approaches -- 4.3 Ground Truth -- 4.4 Comparison and Discussion.
5 Conclusion -- References -- Slice Sequential Network: A Lightweight Unsupervised Point Cloud Completion Network -- 1 Introduction -- 2 Related Work -- 2.1 3D Learning -- 2.2 3D Completion -- 3 Our Method -- 3.1 Overview -- 3.2 Slicer -- 3.3 Multi-scale Point Encoder -- 3.4 Sequential Predictor -- 3.5 Shape Prediction Decoder -- 3.6 Loss Function -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Point Cloud Completion Results -- 4.3 Analysis of Encoder -- 4.4 Robustness to Occlusion -- 4.5 Comparison of Complexity -- 5 Ablation Study -- 6 Conclusion -- References -- From Digital Model to Reality Application: A Domain Adaptation Method for Rail Defect Detection -- 1 Introduction -- 2 Preliminaries -- 3 Method -- 3.1 DT-Based Virtual Data Generation -- 3.2 Dummy-Target Domain -- 3.3 DA-YOLO -- 4 Experiment -- 4.1 Dataset and Evaluation Metrics -- 4.2 Experiment Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- FMixAugment for Semi-supervised Learning with Consistency Regularization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 FMixAugment: MixAugment Combined with FMask -- 3.2 Improved Consistency Regularization -- 3.3 Dynamic Growth Threshold -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion and Future Work -- References -- IDANet: Iterative D-LinkNets with Attention for Road Extraction from High-Resolution Satellite Imagery -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Basic Iteration Module -- 3.3 Iterative Architecture -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 5 Results -- 5.1 Comparison of Road Segmentation Methods -- 5.2 Ablation Experiment -- 5.3 The Influence of Network Iteration -- 6 Conclusion -- References.
Disentangling Deep Network for Reconstructing 3D Object Shapes from Single 2D Images -- 1 Introduction -- 2 Related Works -- 3 Disentangling Deep Network -- 3.1 Network Architecture -- 3.2 Learning Objective Functions -- 3.3 Training Strategy -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Analysis -- 4.3 3D Reconstruction -- 4.4 Effects of 3D Shape Identity -- 5 Conclusion -- References -- AnchorConv: Anchor Convolution for Point Clouds Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 AnchorConv -- 3.2 Anchor Reweighting Module -- 3.3 Network Architectures -- 4 Experiments -- 4.1 Classification on ModelNet40 -- 4.2 ShapeNet Part Segmentation -- 4.3 3D Segmentation of Indoor Scene -- 4.4 3D Segmentation of Outdoor Scene -- 4.5 Ablation Study -- 4.6 Qualitative Results -- 5 Conclusion -- References -- IFR: Iterative Fusion Based Recognizer for Low Quality Scene Text Recognition -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Iterative Collaboration -- 3.2 Fusion Module RRF -- 3.3 Loss Functions -- 3.4 Paired Training Data Generate -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with State-of-the-Arts -- 5 Conclusion -- References -- Immersive Traditional Chinese Portrait Painting: Research on Style Transfer and Face Replacement -- 1 Introduction -- 2 Related Work -- 2.1 Neural Style Transfer -- 2.2 Face Replacement -- 3 The P-CP Method -- 3.1 Network Architecture -- 3.2 Neural Style Transfer Network -- 3.3 Face Replacement -- 4 Experiment -- 4.1 Comparison of Different Traditional Chinese Painting Styles -- 4.2 Image Detail Exploration and Optimization -- 4.3 Improvement of Face Replacement with Style Transfer -- 5 Conclusion -- References -- Multi-camera Extrinsic Auto-calibration Using Pedestrians in Occluded Environments -- 1 Introduction.
2 Related Work -- 3 Calibration Based on 3D Positions -- 3.1 3D Head Positions in Local Camera Coordinates -- 3.2 Registration of 3D Point Sets -- 4 Refinement -- 5 Experiments and Results -- 6 Conclusion -- References -- Dual-Layer Barcodes -- 1 Introduction -- 2 Related Work -- 2.1 Steganography -- 2.2 Watermarking -- 2.3 Barcode -- 3 Method -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Noise Layer -- 3.4 Discriminator -- 4 Experiments and Analysis -- 4.1 Dataset and Experimental Setting -- 4.2 Implementation Details -- 4.3 Metrics -- 5 Discussion -- 6 Conclusion -- References -- Graph Matching Based Robust Line Segment Correspondence for Active Camera Relocalization -- 1 Introduction -- 2 Method -- 2.1 System Overview -- 2.2 Robust Line Segment Matching -- 2.3 Active Camera Relocation -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Analysis of Line Segment Matching -- 3.3 Analysis of Relocalization Accuracy and Convergence Speed -- 3.4 Analysis of Robustness in Hard Scenes -- 4 Conclusion -- References -- Unsupervised Learning Framework for 3D Reconstruction from Face Sketch -- 1 Introduction -- 2 Related Work -- 2.1 Image-to-Image Translation -- 2.2 3D Shape Reconstruction -- 3 Method -- 3.1 Dataset Construction -- 3.2 Network Architecture -- 3.3 Loss Functions -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Quantitative Results and Ablation Study -- 4.3 Qualitative Results -- 5 Conclusion -- References -- HEI-Human: A Hybrid Explicit and Implicit Method for Single-View 3D Clothed Human Reconstruction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Explicit Model -- 3.3 Implicit Model -- 3.4 Loss Functions -- 4 Experiments -- 4.1 Dataset and Protocol -- 4.2 Training Details -- 4.3 Quantitative Results -- 4.4 Qualitative Results -- 4.5 Ablation Studies -- 5 Conclusions -- References.
A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction.
Record Nr. UNISA-996464410803316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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