FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912668747563008 |
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| author | Zhang, Lu Yu, Jiazuo Xiong, Haomiao Hu, Ping Zhuge, Yunzhi Lu, Huchuan He, You |
| author_facet | Zhang, Lu Yu, Jiazuo Xiong, Haomiao Hu, Ping Zhuge, Yunzhi Lu, Huchuan He, You |
| contents | Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs face significant challenges in precisely understanding and localizing visual details in high-resolution images -- particularly when dealing with extra-small objects embedded in cluttered contexts. To address this issue, we propose \textsc{FineRS}, a two-stage MLLM-based reinforcement learning framework for jointly reasoning and segmenting extremely small objects within high-resolution scenes. \textsc{FineRS} adopts a coarse-to-fine pipeline comprising Global Semantic Exploration (GSE) and Localized Perceptual Refinement (LPR). Specifically, GSE performs instruction-guided reasoning to generate a textural response and a coarse target region, while LPR refines this region to produce an accurate bounding box and segmentation mask. To couple the two stages, we introduce a locate-informed retrospective reward, where LPR's outputs are used to optimize GSE for more robust coarse region exploration. % Additionally, we present \textsc{FineRS}-4k, a new dataset for evaluating MLLMs on attribute-level reasoning and pixel-level segmentation on subtle, small-scale targets in complex high-resolution scenes. Experimental results on \textsc{FineRS}-4k and public datasets demonstrate that our method consistently outperforms state-of-the-art MLLM-based approaches on both instruction-guided segmentation and visual reasoning tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_21311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning Zhang, Lu Yu, Jiazuo Xiong, Haomiao Hu, Ping Zhuge, Yunzhi Lu, Huchuan He, You Computer Vision and Pattern Recognition Multi-modal Large Language Models (MLLMs) have shown remarkable capabilities across a wide range of vision-language tasks. However, due to the restricted input resolutions, MLLMs face significant challenges in precisely understanding and localizing visual details in high-resolution images -- particularly when dealing with extra-small objects embedded in cluttered contexts. To address this issue, we propose \textsc{FineRS}, a two-stage MLLM-based reinforcement learning framework for jointly reasoning and segmenting extremely small objects within high-resolution scenes. \textsc{FineRS} adopts a coarse-to-fine pipeline comprising Global Semantic Exploration (GSE) and Localized Perceptual Refinement (LPR). Specifically, GSE performs instruction-guided reasoning to generate a textural response and a coarse target region, while LPR refines this region to produce an accurate bounding box and segmentation mask. To couple the two stages, we introduce a locate-informed retrospective reward, where LPR's outputs are used to optimize GSE for more robust coarse region exploration. % Additionally, we present \textsc{FineRS}-4k, a new dataset for evaluating MLLMs on attribute-level reasoning and pixel-level segmentation on subtle, small-scale targets in complex high-resolution scenes. Experimental results on \textsc{FineRS}-4k and public datasets demonstrate that our method consistently outperforms state-of-the-art MLLM-based approaches on both instruction-guided segmentation and visual reasoning tasks. |
| title | FineRS: Fine-grained Reasoning and Segmentation of Small Objects with Reinforcement Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.21311 |