AI Insight
Researchers developed WeaveFace, a lightweight face detection system that uses a novel "Weave Mamba Fusion" technique to better recognize faces across different scales. The system interleaves adjacent pyramid scales in a pattern that allows efficient cross-scale feature interaction, achieving 91.41% mean accuracy on the WIDER FACE benchmark with only 0.34 million parameters and 1.16 GFLOPs. The approach shows particular strength in detecting faces under challenging conditions like small size, occlusion, and extreme angles, reaching 87.14% accuracy on difficult test cases.
Why it matters
This advancement enables accurate face detection on resource-constrained devices like smartphones and embedded systems, making real-time facial recognition more accessible for security, authentication, and photography applications. The efficient design could accelerate deployment of AI vision systems in edge computing scenarios where computational resources are limited.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: 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, and the way they are combined determines whether cross-scale structure is preserved. Summation collapses the two scales before the scan, so the scan has no cross-scale structure to exploit, while concatenation keeps both scales but at far higher cost. To address this, we propose textbf{Weave Mamba Fusion (WMF)}, which interleaves two adjacent pyramid scales column by column so that each step of a horizontal bidirectional SS2D scan moves from one scale to the other. With partial-channel processing and parameter-free de-weaving, WMF enables efficient cross-scale interaction while preserving feature structure. Integrating WMF into every fusion node yields textbf{WeaveBiFPN}, the neck of our textbf{WeaveFace} detector. On WIDER FACE, WeaveFace achieves 91.41% mean AP with only 0.34M parameters and 1.16 GFLOPs, outperforming prior detectors under 0.5M parameters. Its largest gains are on the Hard subset, where it reaches 87.14% AP. The code is publicly available at url{https://github.com/dohun-mat/WeaveMambaFusion}.
Source: Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection