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arXiv Computer Vision By Caterina Caccavella, Vittorio Fra, Andreas Ziegler, Giulia D'Angelo, Yulia Sandamirskaya

Efficient Semantic Understanding from Digital Foveation

Read the original on arXiv Computer Vision →

The paper proposes a lightweight active‑vision pipeline that mimics biological foveation to perform semantic segmentation more efficiently. By selecting salient fixations, observing high‑resolution foveal patches, and using low‑resolution context, the method achieves 95.9% of baseline Top‑1 accuracy with only 4.7% of the computational cost, and recovers 90.6% of baseline object recall using 58.6% of the computation. The results demonstrate that sparse, selectively allocated observations can yield substantial semantic understanding, suggesting active vision as a viable alternative to uniform dense processing.

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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