Enhancing Sa2VA for Referent Video Object Segmentation: 2nd Solution for 7th LSVOS RVOS Track

• URL: http://arxiv.org/abs/2509.15546v1
• Date: Fri, 19 Sep 2025 03:01:27 GMT
• Title: Enhancing Sa2VA for Referent Video Object Segmentation: 2nd Solution for 7th LSVOS RVOS Track
• Authors: Ran Hong, Feng Lu, Leilei Cao, An Yan, Youhai Jiang, Fengjie Zhu,
• Abstract summary: Referential Video Object (RVOS) aims to segment all objects in a video that match a given natural language description.<n>We present a training-free framework that substantially improves Sa2VA's performance on the RVOS task.
• Score: 11.068687286561177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Referential Video Object Segmentation (RVOS) aims to segment all objects in a video that match a given natural language description, bridging the gap between vision and language understanding. Recent work, such as Sa2VA, combines Large Language Models (LLMs) with SAM~2, leveraging the strong video reasoning capability of LLMs to guide video segmentation. In this work, we present a training-free framework that substantially improves Sa2VA's performance on the RVOS task. Our method introduces two key components: (1) a Video-Language Checker that explicitly verifies whether the subject and action described in the query actually appear in the video, thereby reducing false positives; and (2) a Key-Frame Sampler that adaptively selects informative frames to better capture both early object appearances and long-range temporal context. Without any additional training, our approach achieves a J&F score of 64.14% on the MeViS test set, ranking 2nd place in the RVOS track of the 7th LSVOS Challenge at ICCV 2025.

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