Add model card for EOVSAM

README.md

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+---
+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}, 
+}
+```