Instance Relation

Instance relation research focuses on understanding and leveraging the relationships between individual instances within a dataset, aiming to improve representation learning and downstream tasks. Current efforts concentrate on developing models that effectively capture both local and global instance interactions, employing techniques like hierarchical networks, contrastive learning with relation awareness, and high-order conditional random fields to model complex relationships. This work is significant for advancing various fields, including computer vision (e.g., pose estimation, object detection), and data augmentation in challenging domains like pathology image analysis, by enabling more robust and accurate models.