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)

• Pubblicazione: Cham, Switzerland : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (594 pages)
• Disciplina: 006.4
• Soggetto topico: Pattern recognition systems; Computer vision
• Persona (resp. second.): MaHuimin
• Nota di bibliografia: Includes bibliographical references and index.
• 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.
• Titolo autorizzato: Pattern recognition and computer vision
• ISBN: 3-030-88013-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996464397003316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture notes in computer science ; ; 13022.