Advancing Visual Large Language Model for Multi-granular Versatile Perception

• URL: http://arxiv.org/abs/2507.16213v1
• Date: Tue, 22 Jul 2025 04:09:14 GMT
• Title: Advancing Visual Large Language Model for Multi-granular Versatile Perception
• Authors: Wentao Xiang, Haoxian Tan, Cong Wei, Yujie Zhong, Dengjie Li, Yujiu Yang,
• Abstract summary: We present MVP-LM, a Multi-granular and Versatile Perception framework incorporating Visual Large Language Model.<n>Our framework is designed to integrate both word-based and sentence-based perception tasks alongside box and mask predictions.<n>MVP-LM features an innovative multi-granularity decoder in conjunction with a CoT-inspired dataset unification strategy.
• Score: 31.78788398688894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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