WeaveFace：用 Mamba 交叉尺度融合做轻量人脸检测
Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection
用 Mamba 把金字塔相邻尺度织在一起做轻量人脸检测，0.34M 参数就在 WIDER FACE 上拿到 91.41% mAP，Hard 集也稳，代码还开源了。
论文提出 Weave Mamba Fusion（WMF），将特征金字塔中相邻两个尺度逐列交错，让 SS2D 双向扫描每一步都能跨尺度移动，避免求和造成的结构坍缩或拼接带来的高开销。将 WMF 集成到 BiFPN 各融合节点构成 WeaveBiFPN，形成 WeaveFace 检测器。在 WIDER FACE 上，WeaveFace 以 0.34M 参数和 1.16 GFLOPs 取得 91.41% mAP，在 0.5M 参数以下检测器中领先，Hard 子集 AP 达 87.14%。代码已在 GitHub 公开。
Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection
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}.
