LENS: Learning to Segment Anything with Unified Reinforced Reasoning
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911272824471552 |
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| author | Zhu, Lianghui Ouyang, Bin Zhang, Yuxuan Cheng, Tianheng Hu, Rui Shen, Haocheng Ran, Longjin Chen, Xiaoxin Yu, Li Liu, Wenyu Wang, Xinggang |
| author_facet | Zhu, Lianghui Ouyang, Bin Zhang, Yuxuan Cheng, Tianheng Hu, Rui Shen, Haocheng Ran, Longjin Chen, Xiaoxin Yu, Li Liu, Wenyu Wang, Xinggang |
| contents | Text-prompted image segmentation enables fine-grained visual understanding and is critical for applications such as human-computer interaction and robotics. However, existing supervised fine-tuning methods typically ignore explicit chain-of-thought (CoT) reasoning at test time, which limits their ability to generalize to unseen prompts and domains. To address this issue, we introduce LENS, a scalable reinforcement-learning framework that jointly optimizes the reasoning process and segmentation in an end-to-end manner. We propose unified reinforcement-learning rewards that span sentence-, box-, and segment-level cues, encouraging the model to generate informative CoT rationales while refining mask quality. Using a publicly available 3-billion-parameter vision-language model, i.e., Qwen2.5-VL-3B-Instruct, LENS achieves an average cIoU of 81.2% on the RefCOCO, RefCOCO+, and RefCOCOg benchmarks, outperforming the strong fine-tuned method, i.e., GLaMM, by up to 5.6%. These results demonstrate that RL-driven CoT reasoning significantly enhances text-prompted segmentation and offers a practical path toward more generalizable Segment Anything models (SAM). Code is available at https://github.com/hustvl/LENS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LENS: Learning to Segment Anything with Unified Reinforced Reasoning Zhu, Lianghui Ouyang, Bin Zhang, Yuxuan Cheng, Tianheng Hu, Rui Shen, Haocheng Ran, Longjin Chen, Xiaoxin Yu, Li Liu, Wenyu Wang, Xinggang Computer Vision and Pattern Recognition Artificial Intelligence Text-prompted image segmentation enables fine-grained visual understanding and is critical for applications such as human-computer interaction and robotics. However, existing supervised fine-tuning methods typically ignore explicit chain-of-thought (CoT) reasoning at test time, which limits their ability to generalize to unseen prompts and domains. To address this issue, we introduce LENS, a scalable reinforcement-learning framework that jointly optimizes the reasoning process and segmentation in an end-to-end manner. We propose unified reinforcement-learning rewards that span sentence-, box-, and segment-level cues, encouraging the model to generate informative CoT rationales while refining mask quality. Using a publicly available 3-billion-parameter vision-language model, i.e., Qwen2.5-VL-3B-Instruct, LENS achieves an average cIoU of 81.2% on the RefCOCO, RefCOCO+, and RefCOCOg benchmarks, outperforming the strong fine-tuned method, i.e., GLaMM, by up to 5.6%. These results demonstrate that RL-driven CoT reasoning significantly enhances text-prompted segmentation and offers a practical path toward more generalizable Segment Anything models (SAM). Code is available at https://github.com/hustvl/LENS. |
| title | LENS: Learning to Segment Anything with Unified Reinforced Reasoning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2508.14153 |