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

Cross-Domain Ensemble Distillation for Domain Generalization.- Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels.- Hyperspherical Learning in Multi-Label Classification.- When Active Learning Meets Implicit Semantic Data Augmentation.- VL-LTR: Learning Class-Wise Visual-Linguistic Representation for Long-Tailed Visual Recognition.- Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-of-Distribution Generalization.- Hierarchical Semi-Supervised Contrastive Learning for ContaminationResistant Anomaly Detection.- Tracking by Associating Clips.- RealPatch: A Statistical Matching Framework for Model Patching with Real Samples.- Background-Insensitive Scene Text Recognition with Text Semantic Segmentation.- Semantic Novelty Detection via Relational Reasoning.- Improving Closed and Open-Vocabulary Attribute Prediction Using Transformers.- TrainingVision Transformers with Only 2040 Images.- Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection.- TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs.- Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars.- Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain.- Photo-Realistic Neural Domain Randomization.- Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning.- Tailoring Self-Supervision for Supervised Learning.- Difficulty-Aware Simulator for Open Set Recognition.- Few-Shot Class-Incremental Learning from an Open-Set Perspective.- FOSTER: Feature Boosting and Compression for Class-Incremental Learning.- Visual Knowledge Tracing.- S3C: Self-Supervised Stochastic Classifiers for Few-Shot ClassIncremental Learning.- Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism.- VSA: Learning Varied-Size Window Attention in Vision Transformers.- Unbiased Manifold Augmentation for Coarse Class Subdivision.- DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition.- Rethinking Confidence Calibration for Failure Prediction.- Uncertainty-Guided Source-Free Domain Adaptation.- Should All Proposals Be Treated Equally in Object Detection?.- VIP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers.- incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection.- IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition.- PRIME: A Few Primitives Can Boost Robustness to Common Corruptions.- Rotation Regularization without Rotation.- Towards Accurate Open-Set Recognition via Background-Class Regularization.- In Defense of Image Pre-trainingfor Spatiotemporal Recognition.- Augmenting Deep Classifiers with Polynomial Neural Networks.- Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection.- Online Task-Free Continual Learning with Dynamic Sparse Distributed Memory.