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Sample and Computation Redistribution for Efficient Face Detection

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arxiv 2105.04714 v1 pith:EJNZWBNX submitted 2021-05-10 cs.CV

Jia Guo , Jiankang Deng , Alexandros Lattas , Stefanos Zafeiriou This is my paper
classification cs.CV
keywords computationfacedetectionefficientredistributionsampletrainingaccurate
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Although tremendous strides have been made in uncontrolled face detection, efficient face detection with a low computation cost as well as high precision remains an open challenge. In this paper, we point out that training data sampling and computation distribution strategies are the keys to efficient and accurate face detection. Motivated by these observations, we introduce two simple but effective methods (1) Sample Redistribution (SR), which augments training samples for the most needed stages, based on the statistics of benchmark datasets; and (2) Computation Redistribution (CR), which reallocates the computation between the backbone, neck and head of the model, based on a meticulously defined search methodology. Extensive experiments conducted on WIDER FACE demonstrate the state-of-the-art efficiency-accuracy trade-off for the proposed \scrfd family across a wide range of compute regimes. In particular, \scrfdf{34} outperforms the best competitor, TinaFace, by $3.86\%$ (AP at hard set) while being more than \emph{3$\times$ faster} on GPUs with VGA-resolution images. We also release our code to facilitate future research.

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Cited by 5 Pith papers

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