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

• Record Nr.: UNISA996464395903316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-88004-4
• Descrizione fisica: 1 online resource (634 pages)
• Collana: Lecture Notes in Computer Science ; ; 13019
• Disciplina: 621.367
• Soggetti: Optical data processing; Artificial intelligence; Computer vision
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• 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.

The radical tradition : a study in modern revolutionary thought / / Richard Gombin ; translated by Rupert Swyer

• Record Nr.: UNINA9910969864303321
• Autore: Gombin Richard <1939->
• Pubbl/distr/stampa: London, : Routledge, 2010
• ISBN: 1-136-03622-9; 1-283-84561-X; 1-136-03614-8; 0-203-60983-2
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (163 p.)
• Collana: Routledge revivals
• Disciplina: 321.094; 322.4/2/094; 322.42094
• Soggetti: Communism - Soviet Union - History; Socialism - Soviet Union - History
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: First published in 1978 by Methuen & Co.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; The Radical Tradition: A Study in Modern Revolutionary Thought; Copyright; Contents; Introduction: the alternative; 1 The Soviet State: myths and realities (1917-21); The three myths and their history; Bolshevism and its 'detractors'; 2 The radical tradition in Russia; The growth of Russian socialism in the nineteenth century; Marxism and power: an early critique; 3 Council communism; The First World War and the emergence of new forms of workers' struggle; From left-wing ; radicalism to left-wing communism; Council communism and party communism; 4 The critique of Marxian reification; Notes and referencesBibliography; Index
• Sommario/riassunto: Originally published in 1978, Richard Gombin's book traces the recurrent attitudes in the history of the European revolutionary movement which have criticized socialist and communist parties for their authoritarian and bureaucratic tendencies, and which have stressed spontaneity and decentralization as the correct basis from which to change society.From a critique of Marx, through to an examination of Soviet practice under Lenin, Trotsky and Stalin as a factor in the disillusionment of the left with the methods of the Russian Revolution, Gombin's study examines the concepts of