Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision
The research introduces Motion-Grounded Segment Anything (MoSA), an unsupervised framework to create segmentations without manual labeling. It learns object concepts from unlabeled videos using motion segmentation and contrastive learning, achieving performance comparable to fully supervised models like SAM. This allows for zero-shot segmentation on images by transferring learned object priors.