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arXiv · 2610.15467

Semantic Saliency from Multi-Modal Large Language Model Scene Understanding Maps

Ziqi Wen·Shravan Murlidaran·Jonathan Skaza·William Wang·Miguel P. Eckstein
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Abstract

Image-computable low-level image saliency has shaped perceptual psychology and computer science, but is limited by its inability to capture human high-level cognitive factors that influence attention and eye movements. Here, we propose a new fully automated method to compute semantic saliency by quantifying each object's contribution to scene comprehension without fitting any parameters to human data: the Automated Scene Understanding Map (AUTO-SUM). AUTO-SUM automatically segments and removes individual objects from a scene, uses multimodal large language models (MLLMs) to describe the original and modified scenes, and measures the semantic similarity between these descriptions using text embeddings. We show that AUTO-SUM can predict which object is most important in a human-measured scene understanding map (H-SUM) estimated using human scene descriptions. We also show that AUTO-SUM can predict the object most fixated by human observers during free viewing and scene description tasks better than a low-level saliency model (GBVS, Itti-Koch), a saliency model trained based on human meaning ratings (Meaning Map), and comparable to DeepGaze, which is trained on human fixation data. We contend that AUTO-SUM can be used as a semantic saliency model that complements lower-level saliency models. Code are available at https://github.com/starsky77/AUTO-SUM-Source-Code .

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Ziqi Wen, Shravan Murlidaran, Jonathan Skaza, William Wang, Miguel P. Eckstein. 2026-10-11. Semantic Saliency from Multi-Modal Large Language Model Scene Understanding Maps. https://arxiv.org/abs/2610.15467

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