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arXiv · 2608.02284

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

Haomin Peng·Yongkang Li·Zhaoxiang Liu·Xiaojie Jin·Shiguo Lian·Yunchao Wei·Xinggang Wang
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Abstract

Open-vocabulary segmentation identifies and segments objects from arbitrary textual descriptions. SAM 3 supports noun-phrase-guided segmentation and achieves competitive open-vocabulary performance through exhaustive vocabulary traversal, yet suffers from prohibitive computational overhead as target categories scale. In this paper, we propose an Efficient Open-Vocabulary segmentation framework with SAM 3 (EOVSAM), which adapts SAM 3 for single-pass prediction. EOVSAM removes prompt conditioning to turn SAM 3 into an efficient mask generator and introduces a new Attentional Aggregation strategy to optimize open-vocabulary classification end-to-end. This formulation avoids the multi-stage pipelines and post-processing heuristics commonly used by existing methods, while mitigating the closed-set collapse that can arise when classification is optimized directly. EOVSAM consistently improves segmentation accuracy over vanilla SAM 3 on all evaluated datasets and accelerates inference by up to 338$\times$. Furthermore, EOVSAM maintains high accuracy at lower resolutions while achieving even more remarkable inference speeds. Experiments on standard semantic and panoptic segmentation benchmarks show that EOVSAM combines competitive or state-of-the-art accuracy with a substantial speed advantage over existing open-vocabulary segmentation models. Code and models are available at https://github.com/hustvl/EOVSAM.

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Haomin Peng, Yongkang Li, Zhaoxiang Liu, Xiaojie Jin, Shiguo Lian, Yunchao Wei, Xinggang Wang. 2026-08-03. EOVSAM: Efficient Open-Vocabulary Segmentation with SAM 3 in One Pass. https://arxiv.org/abs/2608.02284

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