Computer Vision - ECCV 2022

Relative Contrastive Loss for Unsupervised Representation Learning.- Fine-Grained Fashion Representation Learning by Online Deep Clustering.- NashAE: Disentangling Representations through Adversarial Covariance Minimization.- A Gyrovector Space Approach for Symmetric Positive Semi-Definite Matrix Learning.- Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training.- Contrasting Quadratic Assignments for Set-Based Representation Learning.- Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer.- Object Discovery and Representation Networks.- Trading Positional Complexity vs Deepness in Coordinate Networks.- MVDG: A Unified Multi-View Framework for Domain Generalization.- Panoptic Scene Graph Generation.- Object-Compositional Neural Implicit Surfaces.- RigNet: Repetitive Image Guided Network for Depth Completion.- FADE: Fusing the Assets of Decoder andEncoder for Task-Agnostic Upsampling.- LiDAL: Inter-Frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation.- Hierarchical Memory Learning for Fine-Grained Scene Graph Generation.- DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation.- MTFormer: Multi-task Learning via Transformer and Cross Task Reasoning.- MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene Flow Estimation with Monocular Images.- TO-Scene: A Large-Scale Dataset for Understanding 3D Tabletop Scenes.- Is It Necessary to Transfer Temporal Knowledge for Domain Adaptive Video Semantic Segmentation?.- Meta Spatio-Temporal Debiasing for Video Scene Graph Generation.- Improving the Reliability for Confidence Estimation.- Fine-Grained Scene Graph Generation with Data Transfer.- Pose2Room: Understanding 3D Scenes from Human Activities.- Towards Hard-Positive Query Mining for