Point Supervision

Point supervision in computer vision aims to train models for tasks like object detection, segmentation, and action localization using only sparse point annotations instead of dense pixel-level labels, significantly reducing annotation costs. Current research focuses on developing novel loss functions (e.g., optimal transport), model architectures (e.g., transformers, dense prediction fields), and training strategies (e.g., self-training, pseudo-labeling) to effectively leverage these limited annotations. This approach is gaining traction due to its potential to improve the efficiency and scalability of training complex vision models, particularly in applications with limited labeled data or high annotation costs.