Gaze Attention: Query-Adaptive Visual Routing for Efficient Multimodal LLMs
When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their intended description. In contrast, current multimodal large language models (MLLMs) attend to all visual tokens, leading to diluted focus and unnecessary computational overhead. Existing efficiency methods often compress or discard visual information before generation, potentially losing details needed for later predictions. In this work, we introduce Gaze Attention, a mechanism that enables MLLMs to select visual regions according to the needs of each generation step. By grouping visual tokens into spatial regions and selecting those relevant ...
| Authors | Junha Song, Byeongho Heo, Geonmo Gu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 2 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.