| 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}, | |
| } | |
| ``` |