Title: RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: attention sinks, diffusion model, referral segmentation, zero-shot

Abstract: Most existing approaches to referring segmentation achieve strong performance
only through fine-tuning or by composing multiple pre-trained models, often at the
cost of additional training and architectural modifications. On the other hand, large-
scale generative diffusion models encode rich semantic information, making them
attractive as general-purpose feature extractors. In this work, we introduce a new
method that directly exploits features, attention scores, from diffusion transformers
for downstream tasks, requiring neither architectural modifications nor additional
training. To systematically evaluate these features, we extend benchmarks with
vision–language grounding tasks spanning both images and videos. Our key insight
is that stop words act as attention magnets: they accumulate surplus attention
and can be filtered to reduce noise. Moreover, we identify global attention sinks
(GAS) emerging in deeper layers and show that they can be safely suppressed or
redirected onto auxiliary tokens, leading to sharper and more accurate grounding
maps. We further propose an attention redistribution strategy, where appended
stop words partition background activations into smaller clusters, yielding sharper
and more localized heatmaps. Building on these findings, we develop RefAM,
a simple training-free grounding framework that combines cross-attention maps,
GAS handling, and redistribution. Across zero-shot referring image and video
segmentation benchmarks, our approach consistently outperforms prior methods,
establishing a new state of the art without fine-tuning or additional components.