The devil is in the object boundary: towards annotation-free instance segmentation using Foundation Models

arXiv:2404.11957v1 Announce Type: new
Abstract: Foundation models, pre-trained on a large amount of data have demonstrated impressive zero-shot capabilities in various downstream tasks. However, in object detection and instance segmentation, two fundamental computer vision tasks heavily reliant on extensive human annotations, foundation models such as SAM and DINO struggle to achieve satisfactory performance. In this study, we reveal that the devil is in the object boundary, \textit{i.e.}, these foundation models fail to discern boundaries between individual objects. For the first …

annotation arxiv cs.cv foundation free instance object segmentation type