Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning
  Segmentation
Authors: name: Su Ho Han, name: Jeongseok Hyun, name: Pilhyeon Lee, name: Minho Shim, name: Dongyoon Wee, name: Seon Joo Kim

Multimodal large language models (MLLMs) demonstrate strong video
understanding by attending to visual tokens relevant to textual queries. To
directly adapt this for localization in a training-free manner, we cast video
reasoning segmentation as a video QA task and extract attention maps via
rollout mechanism. However, raw attention maps are noisy and poorly aligned
with object regions. We propose Decomposed Attention Fusion (DecAF), which
refines these maps through two mechanisms: (1) contrastive object-background
fusion and (2) complementary video-frame fusion. This method suppresses
irrelevant activations and enhances object-focused cues, enabling direct
conversion of attention maps into coarse segmentation masks. In addition, we
introduce attention-guided SAM2 prompting for obtaining fine-grained masks.
Unlike existing methods that jointly train MLLMs with SAM, our method operates
entirely without retraining. DecAF outperforms training-free methods and
achieves performance comparable to training-based methods on both referring and
reasoning VOS benchmarks. The code will be available at
https://github.com/HYUNJS/DecAF.