Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans
Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath. Whether multimodal large language models (MLLMs), given the same foveated input, search as humans do bears on their use as models of human vision and on attention-alignment scores. We compare three general-purpose MLLMs with human eye-movement scanpaths on goal-directed search (COCO-Search18), driving each model fixation by fixation through an identical, human-matched foveated view and assessing it along three axes: the decision of target presence, the efficiency of reaching the
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- LinkedLinked via arxiv author · 85%Mohamed Amine Kerkouri →
“Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans”
- LinkedLinked via arxiv author · 85%Marouane Tliba →
“Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans”
- LinkedLinked via arxiv author · 85%Aladine Chetouani →
“Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans”
- LinkedLinked via arxiv author · 85%Ulas Bagci →
“Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans”
- LinkedLinked via arxiv author · 85%Alessandro Bruno →
“Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans”