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Why It Matters
Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remains difficult, as it requires global cross-scale dependencies that local fusion cannot model. State space models such as Mamba provide global context with linear complexity by scanning features as a sequence, and therefore offer a promising direction for this problem. Nevertheless, such a scan needs the two pyramid scales combined into a single feature map, an...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2610.05865v1 · Indexed 44 minutes ago