Computer Vision – ECCV 2022 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXIV

Improving Vision Transformers by Revisiting High-Frequency Components.- Recurrent Bilinear Optimization for Binary Neural Networks.- Neural Architecture Search for Spiking Neural Networks.- Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification.- DaViT: Dual Attention Vision Transformers.- Optimal Transport for Label-Efficient Visible-Infrared Person Re-identification.- Locality Guidance for Improving Vision Transformers on Tiny Datasets.- Neighborhood Collective Estimation for Noisy Label Identification and Correction.- Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay.- Anti-Retroactive Interference for Lifelong Learning.- Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning.- Dynamic Metric Learning with Cross-Level Concept Distillation.- MENet: A Memory-Based Network with Dual-Branch for Efficient Event Stream Processing.- Out-of-Distribution Detection with Boundary Aware Learning.- Learning Hierarchy Aware Features for Reducing Mistake Severity.- Learning to Detect Every Thing in an Open World.- KVT: k-NN Attention for Boosting Vision Transformers.- Registration Based Few-Shot Anomaly Detection.- Improving Robustness by Enhancing Weak Subnets.- Learning Invariant Visual Representations for Compositional Zero-Shot Learning.- Improving Covariance Conditioning of the SVD Meta-Layer by Orthogonality.- Out-of-Distribution Detection with Semantic Mismatch under Masking.- Data-Free Neural Architecture Search via Recursive Label Calibration.- Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion.- Acknowledging the Unknown for Multi-Label Learning with Single Positive Labels.- AutoMix: Unveiling the Power of Mixup for Stronger Classifiers.- MaxViT: Multi-axis Vision Transformer.- ScalableViT: Rethinking the Context-Oriented Generalization of Vision Transformer.- Three Things Everyone Should Know about Vision Transformers.- DeiT III: Revenge of the ViT.- MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition.- Self-Feature Distillation with Uncertainty Modeling for Degraded Image Recognition.- Novel Class Discovery without Forgetting.- SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification.- Negative Samples Are at Large: Leveraging Hard-Distance Elastic Loss for Re-identification.- Discrete-Constrained Regression for Local Counting Models.- Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed Recognition.- Chairs Can Be Stood On: Overcoming Object Bias in Human-Object Interaction Detection.- A Fast Knowledge Distillation Framework for Visual Recognition.- DICE: Leveraging Sparsification for Out-of-Distribution Detection.- Invariant Feature Learning forGeneralized Long-Tailed Classification.- Sliced Recursive Transformer.