SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
This work addresses the need for cost-effective and scalable segmentation in computer vision by eliminating reliance on expert annotators, representing a significant milestone in self-supervised open-world segmentation.
The paper tackles the problem of open-world entity segmentation without human annotations by introducing SOHES, a self-supervised method that uses visual feature clustering and teacher-student mutual learning to generate pseudo-labels, achieving unprecedented performance in this task.
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 self-correction. Given a pre-trained self-supervised representation, we produce abundant high-quality pseudo-labels through visual feature clustering. Then, we train a segmentation model on the pseudo-labels, and rectify the noises in pseudo-labels via a teacher-student mutual-learning procedure. Beyond segmenting entities, SOHES also captures their constituent parts, providing a hierarchical understanding of visual entities. Using raw images as the sole training data, our method achieves unprecedented performance in self-supervised open-world segmentation, marking a significant milestone towards high-quality open-world entity segmentation in the absence of human-annotated masks. Project page: https://SOHES-ICLR.github.io.