README.md

---
license: apache-2.0
language:
- en
tags:
- MLLM
---

<div align="center">
<h1>✨X-SAM </h1>
<h3>From Segment Anything to Any Segmentation</h3>

[Hao Wang](https://github.com/wanghao9610)<sup>1,2</sup>,[Limeng Qiao](https://scholar.google.com/citations?user=3PFZAg0AAAAJ&hl=en)<sup>3</sup>,[Zequn Jie](https://scholar.google.com/citations?user=4sKGNB0AAAAJ&hl)<sup>3</sup>, [Zhijian Huang](https://zhijian11.github.io/)<sup>1</sup>, [Chengjian Feng](https://fcjian.github.io/)<sup>3</sup>, 

[Qingfang Zheng](https://openreview.net/profile?id=%7EZheng_Qingfang1)<sup>1</sup>, [Lin Ma](https://forestlinma.com/)<sup>3</sup>, [Xiangyuan Lan](https://scholar.google.com/citations?user=c3iwWRcAAAAJ&hl)<sup>2 📧</sup>, [Xiaodan Liang](https://scholar.google.com/citations?user=voxznZAAAAAJ&hl)<sup>1 📧</sup>

<sup>1</sup> Sun Yat-sen University, <sup>2</sup> Peng Cheng Laboratory, <sup>3</sup> Meituan Inc.

<sup>📧</sup> Corresponding author
</div>

<div align="center" style="display: flex; justify-content: center; align-items: center;">
  <a href="https://arxiv.org/abs/2508.04655" style="margin: 0 2px;">
    <img src='https://img.shields.io/badge/arXiv-2508.04655-red?style=flat&logo=arXiv&logoColor=red' alt='arxiv'>
  </a>
  <a href='https://huggingface.co/hao9610/X-SAM' style="margin: 0 2px;">
    <img src='https://img.shields.io/badge/Hugging Face-ckpts-orange?style=flat&logo=HuggingFace&logoColor=orange' alt='huggingface'>
  </a>
  <a href="https://github.com/wanghao9610/X-SAM" style="margin: 0 2px;">
    <img src='https://img.shields.io/badge/GitHub-Repo-blue?style=flat&logo=GitHub' alt='GitHub'>
  </a>
  <a href="http://47.115.200.157:7861" style="margin: 0 2px;">
    <img src='https://img.shields.io/badge/Demo-Gradio-gold?style=flat&logo=Gradio&logoColor=red' alt='Demo'>
  </a>
  <a href='https://wanghao9610.github.io/X-SAM/' style="margin: 0 2px;">
    <img src='https://img.shields.io/badge/🌐_Project-Webpage-green?style=flat&logoColor=white' alt='webpage'>
  </a>
</div>

## 🚀 Introduction

* X-SAM introduces a unified multimodal large language model (MLLM) framework, extending the segmentation paradigm from *segment anything* to *any segmentation*, thereby enhancing pixel-level perceptual understanding.

* X-SAM proposes a novel Visual GrounDed (VGD) segmentation task, which segments all instance objects using interactive visual prompts, empowering the model with visually grounded, pixel-wise interpretative capabilities.

* X-SAM presents a unified training strategy that enables co-training across multiple datasets. Experimental results demonstrate that X-SAM achieves state-of-the-art performance on various image segmentation benchmarks, highlighting its efficiency in multimodal, pixel-level visual understanding.

## 🔖 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 *segment anything* to *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.

👉 **More details can be found in [GitHub](https://github.com/wanghao9610/X-SAM).**

## 📌 Citation
If you find X-SAM is helpful for your research or applications, please consider giving us a like 💖 and citing it by the following BibTex entry.

```bibtex
@article{wang2025xsam,
  title={X-SAM: From Segment Anything to Any Segmentation},
  author={Wang, Hao and Qiao, Limeng and Jie, Zequn and Huang, Zhijian and Feng, Chengjian and Zheng, Qingfang and Ma, Lin and Lan, Xiangyuan and Liang, Xiaodan},
  journal={arXiv preprint arXiv:2508.04655},
  year={2025}
}

```