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[null,"1.","Record Nr.",null,"UNISA996464395903316"]
[null,null,"Titolo",null,"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"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-88004-4"]
[null,null,"Descrizione fisica",null,"1 online resource (634 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; 13019"]
[null,null,"Disciplina",null,"621.367"]
[null,null,"Soggetti",null,"Optical data processing"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computer vision"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Object Detection, Tracking and Recognition -- High-Performance Discriminative Tracking with Target-Aware Feature Embeddings -- 1 Introduction -- 2 Discriminative Tracking with Target-Aware Feature Embeddings -- 2.1 Target-Unaware Feature Extraction -- 2.2 Target-Aware Feature Construction -- 2.3 Ridge Regression with Target-Aware Feature Embeddings -- 2.4 Offline Training -- 2.5 Online Tracking -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Feature Comparisons -- 3.3 State-of-the-Art Comparisons -- 4 Conclusion -- References -- 3D Multi-object Detection and Tracking with Sparse Stationary LiDAR -- 1 Introduction -- 2 Related Work -- 2.1 3D Object Detection -- 2.2 3D Multi-Object Tracking -- 3 Proposed Method -- 3.1 Tracklet Regression -- 3.2 Data Association -- 3.3 Football Game Dataset -- 4 Experiments -- 4.1 Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- CRNet: Centroid Radiation Network for Temporal Action Localization -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Notation and Preliminaries -- 3.2 Feature Extractor Network -- 3.3 Relation Network -- 3.4 Centroids Prediction -- 3.5 Instance Generation -- 3.6 Overall Objective Before Random Walk -- 3.7 Prediction and Post-processing -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation of RelNet, CenNet and OffNet -- 4.4 Performance with Fewer Data -- 4.5 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.1 Experimental Setup -- 4.2 Exploratory Experiments -- 4.3 Comparisons with the State-of-the-art -- 5 Conclusion -- References -- Locality-Constrained Collaborative Representation with Multi-resolution Dictionary for Face Recognition -- 1 Introduction -- 2 Proposed Method -- 2.1 Notations -- 2.2 Model of LCCR-MRD -- 2.3 Optimization -- 2.4 Classification -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Results and Discussions -- 4 Conclusion -- References -- Fast and Fusion: Real-Time Pedestrian Detector Boosted by Body-Head Fusion -- 1 Introdution -- 2 Related Work -- 3 Fast and Fusion -- 3.1 Baseline -- 3.2 Body-Head Fusion -- 3.3 Auxiliary Training Task -- 4 Experiment -- 4.1 Datasets and Evaluation Metric -- 4.2 Evaluation on Extended CityPersons Dataset -- 4.3 Evaluation on CrowdHuman Dataset -- 4.4 Ablation Study -- 5 Conclusion -- References -- STA-GCN: Spatio-Temporal AU Graph Convolution Network for Facial Micro-expression Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Micro-Expression Recognition -- 2.2 Graph Convolution Network -- 3 Method -- 3.1 ROI Division -- 3.2 3D CNN with Non-Local Block -- 3.3 AU-attention Graph Convolution -- 3.4 Loss Function -- 4 Experiment -- 4.1 Experimental Setting -- 4.2 Implementation Details -- 4.3 Experimental Result -- 5 Conclusion -- References -- Attentive Contrast Learning Network for Fine-Grained Classification -- 1 Introduction -- 2 Method -- 2.1 Attention Generator -- 2.2 Contrastive Learning Module -- 2.3 Synergic Learning Module -- 2.4 Learning Attentive Contrast Learning Networks -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparison with Other Methods -- 3.5 Visualization Results -- 4 Conclusion -- References -- Relation-Based Knowledge Distillation for Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 CAE-Based Methods."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"-- 5 Conclusion -- References -- Joint Attention Mechanism for Unsupervised Video Object Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Joint Attention Mechanism -- 3.2 Network Architecture -- 4 Experiments -- 4.1 Datasets and Evaluation -- 4.2 Ablation Study -- 4.3 Comparison to the State-Of-The-Arts -- 5 Conclusion -- References -- Foreground Feature Selection and Alignment for Adaptive Object Detection."]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.5 Implementation, Training and Inference -- 4 Experiments -- 4.1 Ablation Study -- 5 Conclusions -- References -- Attention Template Update Model for Siamese Tracker -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Traditional Update -- 3.2 Network Architecture -- 3.3 Adjustment and Update Blocks -- 3.4 Channel Attention Block -- 3.5 Training Model -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Checkpoint Selection -- 4.3 Performance in Several Benchmarks -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Insight on Attention Modules for Skeleton-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 2.1 Skeleton-Based Action Recognition -- 2.2 Attention Mechanisms -- 3 Multi-category Attention Modules -- 3.1 Spatial-Wise Attention Module -- 3.2 Temporal-Wise Attention Module -- 3.3 Spatiotemporal Attention Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Ablation Studies -- 4.3 Comparison with the State-of-the-Art -- 5 Conclusions -- References -- AO-AutoTrack: Anti-occlusion Real-Time UAV Tracking Based on Spatio-temporal Context -- 1 Introduction -- 2 Related Work -- 2.1 Discriminative Correlation Filter Tracking Algorithm -- 2.2 Anti-occlusion Object Tracking -- 2.3 DCF Onboard UAV -- 3 Proposed Tracking Approach -- 3.1 Review AutoTrack -- 3.2 Temporal Regularization Analysis and Improvement -- 3.3 Re-detection Mechanism -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Comparison with Hand-Crafted Based Trackers -- 4.3 Re-detection "]
[{"$nulls":4},"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."]
[{"$nulls":4},"Scale-Aware Multi-branch Decoder for Salient Object Detection."]
[null,"2.","Record Nr.",null,"UNINA9910811440103321"]
[null,null,"Autore",null,"McCutcheon Chuck"]
[null,null,"Titolo",null,"Dog whistles, walk-backs, and Washington handshakes : decoding the jargon, slang, and bluster of American political speech / / Chuck McCutcheon and David Mark ; foreword by Jeff Greenfield"]
[null,null,"Pubbl/distr/stampa",null,"Lebanon, New Hampshire : , : ForeEdge An Imprint of University Press of New England, , [2014]"]
[{"$nulls":4},"©2014"]
[null,null,"ISBN",null,"1-61168-603-2"]
[{"$nulls":4},"1-61168-657-1"]
[null,null,"Descrizione fisica",null,"1 online resource (270 p.)"]
[null,null,"Disciplina",null,"427.973"]
[null,null,"Soggetti",null,"Political science"]
[{"$nulls":4},"English language - Political aspects - United States"]
[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","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]