{"ID":2891617,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16213","arxiv_id":"2507.16213","title":"Advancing Visual Large Language Model for Multi-granular Versatile Perception","abstract":"Perception is a fundamental task in the field of computer vision, encompassing a diverse set of subtasks that can be systematically categorized into four distinct groups based on two dimensions: prediction type and instruction type. Notably, existing researches often focus solely on a limited subset of these potential combinations, which constrains their applicability and versatility across various contexts. In response to this challenge, we present MVP-LM, a Multi-granular and Versatile Perception framework incorporating Visual Large Language Model. Our framework is designed to integrate both word-based and sentence-based perception tasks alongside box and mask predictions within a single architecture. MVP-LM features an innovative multi-granularity decoder in conjunction with a CoT-inspired dataset unification strategy, enabling seamless supervised fine-tuning across a wide spectrum of tasks, including but not limited to panoptic segmentation, detection, grounding, and referring expression segmentation. Furthermore, we introduce a query enhancement strategy aimed at harnessing the decoding and generative capabilities inherent in VLLMs. Extensive experiments conducted across a range of benchmarks in both word-based and sentence-based perception tasks substantiate the efficacy of our framework. The code will be available at https://github.com/xiangwentao666/MVP-LM.","short_abstract":"Perception is a fundamental task in the field of computer vision, encompassing a diverse set of subtasks that can be systematically categorized into four distinct groups based on two dimensions: prediction type and instruction type. Notably, existing researches often focus solely on a limited subset of these potential...","url_abs":"https://arxiv.org/abs/2507.16213","url_pdf":"https://arxiv.org/pdf/2507.16213v1","authors":"[\"Wentao Xiang\",\"Haoxian Tan\",\"Cong Wei\",\"Yujie Zhong\",\"Dengjie Li\",\"Yujiu Yang\"]","published":"2025-07-22T04:09:14Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":611900,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2891617,"paper_url":"https://arxiv.org/abs/2507.16213","paper_title":"Advancing Visual Large Language Model for Multi-granular Versatile Perception","repo_url":"https://github.com/xiangwentao666/MVP-LM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}