Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning
Multimodal large language models struggle with part-level grounding. The proposed Object-Part Hierarchical Reflective Grounding (OP-HRG) uses a coarse-to-fine reasoning approach, first localizing the parent object then the part, with self-check and re-encoding. A part-aware GRPO framework with stage-wise rewards trains a 4B model that outperforms 7B grounding LLMs and SAM3 on several benchmarks.
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[Submitted on 16 Jul 2026]
Title:Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning
View a PDF of the paper titled Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning, by Kazi Sajeed Mehrab and 6 other authors
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Abstract:Multimodal large language models (MLLMs) ground whole objects well from free-form language queries, but they struggle when the query names a part rather than the object. We trace this to a missing object-part hierarchy, since parts are localized in the same single step used for objects. We propose Object-Part Hierarchical Reflective Grounding (OP-HRG), a coarse-to-fine reasoning-guided grounding strategy that first localizes the parent object and then the part within it. A self-check then reflects on the result, with an extension to re-encode the predicted crop to inspect the region it is correcting. We introduce a part-aware GRPO framework to train our pipeline with stage-wise rewards. A 4B model trained this way outperforms 7B grounding LLMs and SAM3 across PascalPart, PartImageNet, and InstructPart, and transfers to reasoning segmentation.
Comments: ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.15374 [cs.CV]
(or arXiv:2607.15374v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.15374
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kazi Sajeed Mehrab [view email] [v1] Thu, 16 Jul 2026 18:18:31 UTC (25,296 KB)
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