SOHES: Self-supervised Open-world Hierarchical Entity Segmentation

arXiv:2404.12386v1 Announce Type: cross
Abstract: Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive generalization capabilities on unseen images and concepts. Despite its promise, existing entity segmentation methods like Segment Anything Model (SAM) rely heavily on costly expert annotators. This work presents Self-supervised Open-world Hierarchical Entity Segmentation (SOHES), a novel approach that eliminates the need for human annotations. SOHES operates in three phases: self-exploration, self-instruction, and …

arxiv cs.cv cs.lg hierarchical open-world segmentation type world