ATLAS: Learning to Adapt LVLM Attention for Reasoning Video Segmentation
Reasoning Video Object Segmentation (RVOS) aims to localize and segment objects referred to by natural-language expressions throughout a video. Although large vision-language models (LVLMs) implicitly encode localization cues in their attention maps, these attentions are often noisy, diffuse, and scattered across layers, heads, and modalities, limiting their direct use for segmentation. Existing methods typically rely on handcrafted attention-selection heuristics or computationally expensive attention refinement. We propose ATLAS, an attention adaptation framework that learns to transform raw LVLM attentions into object-centric localization cues. ATLAS treats attentions from all layers, h...
| Authors | Hamidreza Dastmalchi, Ali Cheraghian, Aijun An et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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