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Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIV
| Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIV |
| Autore | Avidan Shai |
| Pubbl/distr/stampa | Cham : , : Springer, , 2022 |
| Descrizione fisica | 1 online resource (803 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
BrostowGabriel
CisséMoustapha FarinellaGiovanni Maria HassnerTal |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 3-031-20053-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Organization -- Contents - Part XXIV -- Improving Vision Transformers by Revisiting High-Frequency Components -- 1 Introduction -- 2 Related Work -- 3 Revisiting ViT Models from a Frequency Perspective -- 4 The Proposed Method -- 4.1 Adversarial Training with High-Frequency Perturbations -- 4.2 A Case Study Using ViT-B -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results on ImageNet Classification -- 5.3 Results on Out-of-distribution Data -- 5.4 Transfer Learning to Downstream Tasks -- 5.5 Ablation Studies -- 5.6 Discussions -- 6 Conclusions and Future Work -- References -- Recurrent Bilinear Optimization for Binary Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Bilinear Model of BNNs -- 3.3 Recurrent Bilinear Optimization -- 3.4 Discussion -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Image Classification -- 4.4 Object Detection -- 4.5 Deployment Efficiency -- 5 Conclusion -- References -- Neural Architecture Search for Spiking Neural Networks -- 1 Introduction -- 2 Related Work -- 2.1 Spiking Neural Networks -- 2.2 Neural Architecture Search -- 3 Preliminaries -- 3.1 Leaky Integrate-and-Fire Neuron -- 3.2 NAS Without Training -- 4 Methodology -- 4.1 Linear Regions from LIF Neurons -- 4.2 Sparsity-Aware Hamming Distance -- 4.3 Searching Forward and Backward Connections -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Performance Comparison -- 5.3 Experimental Analysis -- 6 Conclusion -- References -- Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification -- 1 Introduction -- 2 Related Work -- 2.1 Fine-Grained Visual Classification -- 2.2 Human Attention in Vision -- 3 Approach -- 3.1 Overview -- 3.2 Region Feature Mining Module.
3.3 Cross-Hierarchical Orthogonal Fusion Module -- 4 Experiments and Analysis -- 4.1 Datasets -- 4.2 Hierarchy Interaction Analysis -- 4.3 Evaluation on Traditional FGVC Setting -- 4.4 Further Analysis -- 5 Conclusions -- References -- DaViT: Dual Attention Vision Transformers -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Spatial Window Attention -- 3.3 Channel Group Attention -- 3.4 Model Instantiation -- 4 Analysis -- 5 Experiments -- 5.1 Image Classification -- 5.2 Object Detection and Instance Segmentation -- 5.3 Semantic Segmentation on ADE20k -- 5.4 Ablation Study -- 6 Conclusion -- References -- Optimal Transport for Label-Efficient Visible-Infrared Person Re-Identification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation and Overview -- 3.2 Discrepancy Elimination Network (DEN) -- 3.3 Optimal-Transport Label Assignment (OTLA) -- 3.4 Prediction Alignment Learning (PAL) -- 3.5 Optimization -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 Main Results -- 4.4 Ablation Study -- 4.5 Discussion -- 5 Conclusion -- References -- Locality Guidance for Improving Vision Transformers on Tiny Datasets -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 The Overall Approach -- 3.2 Guidance Positions -- 3.3 Architecture of the CNN -- 4 Experiments -- 4.1 Main Results -- 4.2 Discussion -- 4.3 Ablation Study -- 5 Conclusion -- References -- Neighborhood Collective Estimation for Noisy Label Identification and Correction -- 1 Introduction -- 2 Related Work -- 2.1 Noise Verification -- 2.2 Label Correction -- 3 The Proposed Method -- 3.1 Neighborhood Collective Noise Verification -- 3.2 Neighborhood Collective Label Correction -- 3.3 Training Objectives -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparisons with the State of the Art -- 4.3 Analysis. 5 Conclusions -- References -- Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay -- 1 Introduction -- 2 Related Works -- 2.1 Class-Incremental Learning -- 2.2 Few-Shot Class-Incremental Learning -- 2.3 Data-Free Knowledge Distillation -- 3 Preliminaries -- 3.1 Problem Setting -- 3.2 Data-Free Replay -- 4 Methodology -- 4.1 Entropy-Regularized Data-Free Replay -- 4.2 Learning Incrementally with Uncertain Data -- 5 Experiments -- 5.1 Datasets -- 5.2 Implementation Details -- 5.3 Re-implementation of Replay-based Methods -- 5.4 Main Results and Comparison -- 5.5 Analysis -- 6 Conclusion -- References -- Anti-retroactive Interference for Lifelong Learning -- 1 Introduction -- 2 Related Work -- 2.1 Lifelong Learning -- 2.2 Adversarial Training -- 3 Proposed Method -- 3.1 Extracting Intra-Class Features -- 3.2 Generating and Fusing Task-Specific Models -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results and Comparison -- 4.4 Ablation Study -- 5 Conclusion -- References -- Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Build vMF Classifier on Hyper-Sphere -- 3.2 Quantify Distribution Overlap Coefficient on Hyper-Sphere -- 3.3 Improve Representation of Feature and Classifier via o -- 3.4 Calibrate Classifier Weight Beyond Training via o -- 4 Experiments -- 4.1 Long-Tailed Image Classification Task -- 4.2 Long-Tailed Semantic and Instance Segmentation Task -- 4.3 Ablation Study -- 5 Conclusions -- References -- Dynamic Metric Learning with Cross-Level Concept Distillation -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Dynamic Metric Learning -- 3.2 Hierarchical Concept Refiner -- 3.3 Cross-Level Concept Distillation -- 3.4 Discussions -- 4 Experiments. 4.1 Datasets -- 4.2 Evaluation Protocol -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Experimental Analysis -- 5 Conclusion -- References -- MENet: A Memory-Based Network with Dual-Branch for Efficient Event Stream Processing -- 1 Introduction -- 2 Related Work -- 2.1 Event-Based Representations -- 2.2 Memory-Based Networks -- 3 Event Camera Model -- 4 Method -- 4.1 Dual-Branch Structure -- 4.2 Double Polarities Calculation Method -- 4.3 Point-Wise Memory Bank -- 4.4 Training and Testing Strategies -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Ablation Study -- 5.3 Object Recognition -- 5.4 Gesture Recognition -- 6 Conclusion -- References -- Out-of-distribution Detection with Boundary Aware Learning -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Boundary Aware Learning -- 4.1 Representation Extraction Module (REM) -- 4.2 Representation Sampling Module (RSM) -- 4.3 Representation Discrimination Module (RDM) -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Ablation Study -- 5.4 Detection Results -- 5.5 Visualization of trivial and hard OOD features -- 6 Conclusion -- References -- Learning Hierarchy Aware Features for Reducing Mistake Severity -- 1 Introduction -- 2 Related Work -- 3 HAF: Proposed Approach -- 3.1 Fine Grained Cross-Entropy (LCEfine) -- 3.2 Soft Hierarchical Consistency (Lshc) -- 3.3 Margin Loss (Lm) -- 3.4 Geometric Consistency (Lgc) -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Training Configurations -- 4.3 Results -- 4.4 Coarse Classification Accuracy -- 5 Analysis -- 5.1 Ablation Study -- 5.2 Mistakes Severity Plots -- 5.3 Discussion: Hierarchical Metrics -- 6 Conclusion -- References -- Learning to Detect Every Thing in an Open World -- 1 Introduction -- 2 Related Work -- 3 Learning to Detect Every Thing -- 3.1 Data Augmentation: Background Erasing (BackErase). 3.2 Decoupled Multi-domain Training -- 4 Experiments -- 4.1 Cross-category Generalization -- 4.2 Cross-Dataset Generalization -- 5 Conclusion -- References -- KVT: k-NN Attention for Boosting Vision Transformers -- 1 Introduction -- 2 Related Work -- 2.1 Self-attention -- 2.2 Transformer for Vision -- 3 k-NN Attention -- 3.1 Vanilla Attention -- 3.2 k-NN Attention -- 3.3 Theoretical Analysis on k-NN Attention -- 4 Experiments for Vision Transformers -- 4.1 Experimental Settings -- 4.2 Results on ImageNet -- 4.3 The Impact of Number k -- 4.4 Convergence Speed of k-NN Attention -- 4.5 Other Properties of k-NN Attention -- 4.6 Comparisons with Temperature in Softmax -- 4.7 Visualization -- 4.8 Object Detection and Semantic Segmentation -- 5 Conclusion -- References -- Registration Based Few-Shot Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Few-Shot Learning -- 2.3 Few-Shot Anomaly Detection -- 3 Problem Setting -- 4 Method -- 4.1 Feature Registration Network -- 4.2 Normal Distribution Estimation -- 4.3 Inference -- 5 Experiments -- 5.1 Experimental Setups -- 5.2 Comparison with State-of-the-Art Methods -- 5.3 Ablation Studies -- 5.4 Visualization Analysis -- 6 Conclusion -- References -- Improving Robustness by Enhancing Weak Subnets -- 1 Introduction -- 2 Related Work -- 3 EWS: Training by Enhancing Weak Subnets -- 3.1 Subnet Construction and Impact on Overall Performance -- 3.2 Finding Particularly Weak Subnets -- 3.3 EWS: Enhancing Weak Subnets with Knowledge Distillation -- 3.4 Combining EWS with Adversarial Training -- 4 Experiments -- 4.1 Improving Corruption Robustness -- 4.2 Improving Adversarial Robustness -- 5 Ablation and Discussions -- 5.1 Search Strategies and Hyper-Parameters -- 5.2 Vulnerability of Blocks and Layers -- 6 Conclusion -- References. Learning Invariant Visual Representations for Compositional Zero-Shot Learning. |
| Record Nr. | UNISA-996500065903316 |
Avidan Shai
|
||
| Cham : , : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII
| Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII |
| Autore | Avidan Shai |
| Pubbl/distr/stampa | Cham : , : Springer, , 2022 |
| Descrizione fisica | 1 online resource (800 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
BrostowGabriel
CisséMoustapha FarinellaGiovanni Maria HassnerTal |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 3-031-19790-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996495565403316 |
|
Avidan Shai
|
||
| Cham : , : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIV
| Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXIV |
| Autore | Avidan Shai |
| Pubbl/distr/stampa | Cham : , : Springer, , 2022 |
| Descrizione fisica | 1 online resource (803 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
BrostowGabriel
CisséMoustapha FarinellaGiovanni Maria HassnerTal |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 3-031-20053-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Organization -- Contents - Part XXIV -- Improving Vision Transformers by Revisiting High-Frequency Components -- 1 Introduction -- 2 Related Work -- 3 Revisiting ViT Models from a Frequency Perspective -- 4 The Proposed Method -- 4.1 Adversarial Training with High-Frequency Perturbations -- 4.2 A Case Study Using ViT-B -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results on ImageNet Classification -- 5.3 Results on Out-of-distribution Data -- 5.4 Transfer Learning to Downstream Tasks -- 5.5 Ablation Studies -- 5.6 Discussions -- 6 Conclusions and Future Work -- References -- Recurrent Bilinear Optimization for Binary Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Bilinear Model of BNNs -- 3.3 Recurrent Bilinear Optimization -- 3.4 Discussion -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Image Classification -- 4.4 Object Detection -- 4.5 Deployment Efficiency -- 5 Conclusion -- References -- Neural Architecture Search for Spiking Neural Networks -- 1 Introduction -- 2 Related Work -- 2.1 Spiking Neural Networks -- 2.2 Neural Architecture Search -- 3 Preliminaries -- 3.1 Leaky Integrate-and-Fire Neuron -- 3.2 NAS Without Training -- 4 Methodology -- 4.1 Linear Regions from LIF Neurons -- 4.2 Sparsity-Aware Hamming Distance -- 4.3 Searching Forward and Backward Connections -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Performance Comparison -- 5.3 Experimental Analysis -- 6 Conclusion -- References -- Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification -- 1 Introduction -- 2 Related Work -- 2.1 Fine-Grained Visual Classification -- 2.2 Human Attention in Vision -- 3 Approach -- 3.1 Overview -- 3.2 Region Feature Mining Module.
3.3 Cross-Hierarchical Orthogonal Fusion Module -- 4 Experiments and Analysis -- 4.1 Datasets -- 4.2 Hierarchy Interaction Analysis -- 4.3 Evaluation on Traditional FGVC Setting -- 4.4 Further Analysis -- 5 Conclusions -- References -- DaViT: Dual Attention Vision Transformers -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Spatial Window Attention -- 3.3 Channel Group Attention -- 3.4 Model Instantiation -- 4 Analysis -- 5 Experiments -- 5.1 Image Classification -- 5.2 Object Detection and Instance Segmentation -- 5.3 Semantic Segmentation on ADE20k -- 5.4 Ablation Study -- 6 Conclusion -- References -- Optimal Transport for Label-Efficient Visible-Infrared Person Re-Identification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation and Overview -- 3.2 Discrepancy Elimination Network (DEN) -- 3.3 Optimal-Transport Label Assignment (OTLA) -- 3.4 Prediction Alignment Learning (PAL) -- 3.5 Optimization -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Implementation Details -- 4.3 Main Results -- 4.4 Ablation Study -- 4.5 Discussion -- 5 Conclusion -- References -- Locality Guidance for Improving Vision Transformers on Tiny Datasets -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 The Overall Approach -- 3.2 Guidance Positions -- 3.3 Architecture of the CNN -- 4 Experiments -- 4.1 Main Results -- 4.2 Discussion -- 4.3 Ablation Study -- 5 Conclusion -- References -- Neighborhood Collective Estimation for Noisy Label Identification and Correction -- 1 Introduction -- 2 Related Work -- 2.1 Noise Verification -- 2.2 Label Correction -- 3 The Proposed Method -- 3.1 Neighborhood Collective Noise Verification -- 3.2 Neighborhood Collective Label Correction -- 3.3 Training Objectives -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparisons with the State of the Art -- 4.3 Analysis. 5 Conclusions -- References -- Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay -- 1 Introduction -- 2 Related Works -- 2.1 Class-Incremental Learning -- 2.2 Few-Shot Class-Incremental Learning -- 2.3 Data-Free Knowledge Distillation -- 3 Preliminaries -- 3.1 Problem Setting -- 3.2 Data-Free Replay -- 4 Methodology -- 4.1 Entropy-Regularized Data-Free Replay -- 4.2 Learning Incrementally with Uncertain Data -- 5 Experiments -- 5.1 Datasets -- 5.2 Implementation Details -- 5.3 Re-implementation of Replay-based Methods -- 5.4 Main Results and Comparison -- 5.5 Analysis -- 6 Conclusion -- References -- Anti-retroactive Interference for Lifelong Learning -- 1 Introduction -- 2 Related Work -- 2.1 Lifelong Learning -- 2.2 Adversarial Training -- 3 Proposed Method -- 3.1 Extracting Intra-Class Features -- 3.2 Generating and Fusing Task-Specific Models -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results and Comparison -- 4.4 Ablation Study -- 5 Conclusion -- References -- Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Build vMF Classifier on Hyper-Sphere -- 3.2 Quantify Distribution Overlap Coefficient on Hyper-Sphere -- 3.3 Improve Representation of Feature and Classifier via o -- 3.4 Calibrate Classifier Weight Beyond Training via o -- 4 Experiments -- 4.1 Long-Tailed Image Classification Task -- 4.2 Long-Tailed Semantic and Instance Segmentation Task -- 4.3 Ablation Study -- 5 Conclusions -- References -- Dynamic Metric Learning with Cross-Level Concept Distillation -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Dynamic Metric Learning -- 3.2 Hierarchical Concept Refiner -- 3.3 Cross-Level Concept Distillation -- 3.4 Discussions -- 4 Experiments. 4.1 Datasets -- 4.2 Evaluation Protocol -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Experimental Analysis -- 5 Conclusion -- References -- MENet: A Memory-Based Network with Dual-Branch for Efficient Event Stream Processing -- 1 Introduction -- 2 Related Work -- 2.1 Event-Based Representations -- 2.2 Memory-Based Networks -- 3 Event Camera Model -- 4 Method -- 4.1 Dual-Branch Structure -- 4.2 Double Polarities Calculation Method -- 4.3 Point-Wise Memory Bank -- 4.4 Training and Testing Strategies -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Ablation Study -- 5.3 Object Recognition -- 5.4 Gesture Recognition -- 6 Conclusion -- References -- Out-of-distribution Detection with Boundary Aware Learning -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Boundary Aware Learning -- 4.1 Representation Extraction Module (REM) -- 4.2 Representation Sampling Module (RSM) -- 4.3 Representation Discrimination Module (RDM) -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Ablation Study -- 5.4 Detection Results -- 5.5 Visualization of trivial and hard OOD features -- 6 Conclusion -- References -- Learning Hierarchy Aware Features for Reducing Mistake Severity -- 1 Introduction -- 2 Related Work -- 3 HAF: Proposed Approach -- 3.1 Fine Grained Cross-Entropy (LCEfine) -- 3.2 Soft Hierarchical Consistency (Lshc) -- 3.3 Margin Loss (Lm) -- 3.4 Geometric Consistency (Lgc) -- 4 Experiments and Results -- 4.1 Experimental Setup -- 4.2 Training Configurations -- 4.3 Results -- 4.4 Coarse Classification Accuracy -- 5 Analysis -- 5.1 Ablation Study -- 5.2 Mistakes Severity Plots -- 5.3 Discussion: Hierarchical Metrics -- 6 Conclusion -- References -- Learning to Detect Every Thing in an Open World -- 1 Introduction -- 2 Related Work -- 3 Learning to Detect Every Thing -- 3.1 Data Augmentation: Background Erasing (BackErase). 3.2 Decoupled Multi-domain Training -- 4 Experiments -- 4.1 Cross-category Generalization -- 4.2 Cross-Dataset Generalization -- 5 Conclusion -- References -- KVT: k-NN Attention for Boosting Vision Transformers -- 1 Introduction -- 2 Related Work -- 2.1 Self-attention -- 2.2 Transformer for Vision -- 3 k-NN Attention -- 3.1 Vanilla Attention -- 3.2 k-NN Attention -- 3.3 Theoretical Analysis on k-NN Attention -- 4 Experiments for Vision Transformers -- 4.1 Experimental Settings -- 4.2 Results on ImageNet -- 4.3 The Impact of Number k -- 4.4 Convergence Speed of k-NN Attention -- 4.5 Other Properties of k-NN Attention -- 4.6 Comparisons with Temperature in Softmax -- 4.7 Visualization -- 4.8 Object Detection and Semantic Segmentation -- 5 Conclusion -- References -- Registration Based Few-Shot Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Few-Shot Learning -- 2.3 Few-Shot Anomaly Detection -- 3 Problem Setting -- 4 Method -- 4.1 Feature Registration Network -- 4.2 Normal Distribution Estimation -- 4.3 Inference -- 5 Experiments -- 5.1 Experimental Setups -- 5.2 Comparison with State-of-the-Art Methods -- 5.3 Ablation Studies -- 5.4 Visualization Analysis -- 6 Conclusion -- References -- Improving Robustness by Enhancing Weak Subnets -- 1 Introduction -- 2 Related Work -- 3 EWS: Training by Enhancing Weak Subnets -- 3.1 Subnet Construction and Impact on Overall Performance -- 3.2 Finding Particularly Weak Subnets -- 3.3 EWS: Enhancing Weak Subnets with Knowledge Distillation -- 3.4 Combining EWS with Adversarial Training -- 4 Experiments -- 4.1 Improving Corruption Robustness -- 4.2 Improving Adversarial Robustness -- 5 Ablation and Discussions -- 5.1 Search Strategies and Hyper-Parameters -- 5.2 Vulnerability of Blocks and Layers -- 6 Conclusion -- References. Learning Invariant Visual Representations for Compositional Zero-Shot Learning. |
| Record Nr. | UNINA-9910629291203321 |
|
Avidan Shai
|
||
| Cham : , : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII
| Computer Vision - ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII |
| Autore | Avidan Shai |
| Pubbl/distr/stampa | Cham : , : Springer, , 2022 |
| Descrizione fisica | 1 online resource (800 pages) |
| Disciplina | 006.37 |
| Altri autori (Persone) |
BrostowGabriel
CisséMoustapha FarinellaGiovanni Maria HassnerTal |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato |
Engineering
Technology & Engineering |
| ISBN | 3-031-19790-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619273903321 |
|
Avidan Shai
|
||
| Cham : , : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||