Decomposed Attention Fusion in MLLMs for Training-free Video Reasoning Segmentation

Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to instructions. To exploit this for training-free localization, we cast video reasoning segmentation as video QA and extract attention maps via rollout. Since raw maps are too noisy to represent objects, we propose Decomposed Attention Fusion (DecAF), combining (1) contrastive object-background fusion and (2) complementary video-frame fusion. This yields cleaner attention maps focused on the target object, which can be directly converted into coarse segmentation masks and outperform existing methods. In addition, we introduce attention-guided SAM2 prompting for fine-grained masks, achieving performance comparable to training-based methods on both referring and reasoning VOS benchmarks.