---
pipeline_tag: image-segmentation
license: other
---

# EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass

**EOVSAM** is an efficient open-vocabulary segmentation framework built on SAM 3 that adapts SAM 3 for single-pass prediction.

- **Paper:** [EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass](https://huggingface.co/papers/2608.02284)
- **Code:** [GitHub Repository](https://github.com/hustvl/EOVSAM)

## Overview

EOVSAM removes prompt conditioning to turn SAM 3 into an efficient mask generator and introduces an Attentional Aggregation strategy to optimize open-vocabulary classification end-to-end. This formulation avoids multi-stage pipelines and post-processing heuristics while consistently improving segmentation accuracy over vanilla SAM 3 and accelerating inference by up to 338×.

## Usage

Please refer to the official [EOVSAM GitHub repository](https://github.com/hustvl/EOVSAM) for installation, dataset preparation, training, and evaluation scripts.

## License

This project is a composite distribution incorporating NVIDIA RADIO, SAM 3, and MAFT-Plus components; each remains subject to its upstream terms. Please check the [GitHub License section](https://github.com/hustvl/EOVSAM#license) for details.

## Citation

```bibtex
@misc{peng2026eovsamefficientopenvocabularysegmentation,
      title={EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass}, 
      author={Haomin Peng and Yongkang Li and Zhaoxiang Liu and Xiaojie Jin and Shiguo Lian and Yunchao Wei and Xinggang Wang},
      year={2026},
      eprint={2608.02284},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.02284}, 
}
```