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

arXiv:2310.10912v3 Announce Type: replace
Abstract: The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of natural language processing. This paper delves into the exploration of open-world segmentation, presenting a novel approach called Image Prompt Segmentation (IPSeg) that harnesses the power of vision foundational models. IPSeg lies the principle of a training-free paradigm, which capitalizes on image prompt techniques. Specifically, IPSeg utilizes a …

abstract arxiv computer computer vision cs.cv domain exploration foundation foundational foundational models free image influence language language models language processing large language large language models natural natural language natural language processing novel open-world paper paradigm presenting processing prompt realm replace segmentation shift training type via vision world