Face detection is a fundamental problem for many downstream face
applications, and there is a rising demand for faster, more accurate yet
support for higher resolution face detectors. Recent smartphones can record a
video in 8K resolution, but many of the existing face detectors still fail due
to the anchor size and training data. We analyze the failure cases and observe
a large number of correct predicted boxes with incorrect confidences. To
calibrate these confidences, we propose a confidence ranking network with a
pairwise ranking loss to re-rank the predicted confidences locally within the
same image. Our confidence ranker is model-agnostic, so we can augment the data
by choosing the pairs from multiple face detectors during the training, and
generalize to a wide range of face detectors during the testing. On WiderFace,
we achieve the highest AP on the single-scale, and our AP is competitive with
the previous multi-scale methods while being significantly faster. On 8K
resolution, our method solves the GPU memory issue and allows us to indirectly
train on 8K. We collect 8K resolution test set to show the improvement, and we
will release our test set as a new benchmark for future research.

该研究提出了一种置信度排名网络，可校准同一图像内正确预测的盒子的置信度，并在 8K 分辨率下具有更高的 AP 表现，成为 WiderFace 基准测验的竞争者。