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arXiv Computer Vision By Valentin Hassler, Jonathan Henrich, Yoha\"i-Eliel Berreby, Haoze Zhu, Alexander Ecker

FoAViS: Analyzing Learned Fixation Strategies in a Foveated Active Vision System

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FoAViS is a foveated active vision system that samples visual input using a log‑polar foveation and stores information in a 2D spatial memory. The system’s fixation policy is trained concurrently with tasks such as classification and semantic segmentation, allowing the same memory to be read out globally or densely without architectural changes. Learned fixation policies outperform non‑learned heuristics, improving task performance and producing scanpaths that resemble human gaze behavior.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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