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 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/magic-research/Sa2VA.
Niu et al. (Sun,) studied this question.
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