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arXiv · 2509.16972

The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA

Quanzhu Niu·Dengxian Gong·Shihao Chen·Tao Zhang·Yikang Zhou·Haobo Yuan·Lu Qi·Xiangtai Li·Shunping Ji
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Abstract

Referring video object segmentation (RVOS) requires segmenting and tracking objects in videos conditioned on natural-language expressions, demanding fine-grained understanding of both appearance and motion. Building on Sa2VA, which couples a Multi-modal Large Language Model (MLLM) with the video segmentation model SAM2, we identify two key bottlenecks that limit segmentation performance: sparse frame sampling and reliance on a single [SEG] token for an entire video. We propose Segmentation Augmented and Selective Averaged Sa2VA (SaSaSa2VA) to address these issues. On the 7th LSVOS Challenge (RVOS track), SaSaSa2VA achieves a $\mathcal{J\&F}$ of 67.45, ranking first and surpassing the runner-up by 2.80 points. This result and ablation studies demonstrate that efficient segmentation augmentation and test-time ensembling substantially enhance grounded MLLMs for RVOS. The code is released in Sa2VA repository: https://github.com/bytedance/Sa2VA.

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Quanzhu Niu, Dengxian Gong, Shihao Chen, Tao Zhang, Yikang Zhou, Haobo Yuan, Lu Qi, Xiangtai Li, Shunping Ji. 2025-10-20. The 1st Solution for 7th LSVOS RVOS Track: SaSaSa2VA. https://arxiv.org/abs/2509.16972

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