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The realm of computer vision has witnessed a paradigm shift with the adventof foundational models, mirroring the transformative influence of largelanguage models in the domain of natural language processing. This paper delvesinto the exploration of open-world segmentation, presenting a novel approachcalled Image Prompt Segmentation (IPSeg) that harnesses the power of visionfoundational models. IPSeg lies the principle of a training-free paradigm,which capitalizes on image prompt techniques. Specifically, IPSeg utilizes asingle image containing a subjective visual concept as a flexible prompt toquery vision foundation models like DINOv2 and Stable Diffusion. Our approachextracts robust features for the prompt image and input image, then matches theinput representations to the prompt representations via a novel featureinteraction module to generate point prompts highlighting target objects in theinput image. The generated point prompts are further utilized to guide theSegment Anything Model to segment the target object in the input image. Theproposed method stands out by eliminating the need for exhaustive trainingsessions, thereby offering a more efficient and scalable solution. Experimentson COCO, PASCAL VOC, and other datasets demonstrate IPSeg's efficacy forflexible open-world segmentation using intuitive image prompts. This workpioneers tapping foundation models for open-world understanding through visualconcepts conveyed in images.

Towards Training-free Open-world Segmentation via Image Prompt Foundation Models