{"ID":2885916,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04655","arxiv_id":"2508.04655","title":"X-SAM: From Segment Anything to Any Segmentation","abstract":"Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although the Segment Anything Model (SAM) represents a significant advancement in visual-prompt-driven image segmentation, it exhibits notable limitations in multi-mask prediction and category-specific segmentation tasks, and it cannot integrate all segmentation tasks within a unified model architecture. To address these limitations, we present X-SAM, a streamlined Multimodal Large Language Model (MLLM) framework that extends the segmentation paradigm from \\textit{segment anything} to \\textit{any segmentation}. Specifically, we introduce a novel unified framework that enables more advanced pixel-level perceptual comprehension for MLLMs. Furthermore, we propose a new segmentation task, termed Visual GrounDed (VGD) segmentation, which segments all instance objects with interactive visual prompts and empowers MLLMs with visual grounded, pixel-wise interpretative capabilities. To enable effective training on diverse data sources, we present a unified training strategy that supports co-training across multiple datasets. Experimental results demonstrate that X-SAM achieves state-of-the-art performance on a wide range of image segmentation benchmarks, highlighting its efficiency for multimodal, pixel-level visual understanding. Code is available at https://github.com/wanghao9610/X-SAM.","short_abstract":"Large Language Models (LLMs) demonstrate strong capabilities in broad knowledge representation, yet they are inherently deficient in pixel-level perceptual understanding. Although the Segment Anything Model (SAM) represents a significant advancement in visual-prompt-driven image segmentation, it exhibits notable limita...","url_abs":"https://arxiv.org/abs/2508.04655","url_pdf":"https://arxiv.org/pdf/2508.04655v2","authors":"[\"Hao Wang\",\"Limeng Qiao\",\"Zequn Jie\",\"Zhijian Huang\",\"Chengjian Feng\",\"Qingfang Zheng\",\"Lin Ma\",\"Xiangyuan Lan\",\"Xiaodan Liang\"]","published":"2025-08-06T17:19:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":611257,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2885916,"paper_url":"https://arxiv.org/abs/2508.04655","paper_title":"X-SAM: From Segment Anything to Any Segmentation","repo_url":"https://github.com/wanghao9610/X-SAM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}