arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2103.07017cs.CV
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CRFace:用于模型无关的人脸检测改进的置信度排序器

CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement

Microsoft Cloud&AI(微软云与人工智能部门)

Noranart Vesdapunt, Baoyuan Wang

摘要:

人脸检测是许多下游人脸应用的基本问题,并且对更快,更准确但支持更高分辨率的人脸检测器的需求不断增加。最近的智能手机可以以8K分辨率录制视频,但由于锚点大小和训练数据,许多现有的人脸检测器仍然失败。我们分析了失败的情况下,观察到大量的正确的预测框不正确的信心。为了校准这些置信度,我们提出了一个置信度排名网络与成对的排名损失重新排名预测的置信度在同一图像中的本地。我们的置信度排名器是模型不可知的,因此我们可以在训练过程中通过从多个人脸检测器中选择对来增加数据,并在测试过程中推广到各种人脸检测器。在WiderFace上,我们在单尺度上实现了最高的AP,我们的AP与以前的多尺度方法相比具有竞争力,同时速度明显更快。在8K分辨率上,我们的方法解决了GPU内存问题,并允许我们间接地在8K上进行训练。我们收集了8K分辨率的测试集来展示改进,我们将发布我们的测试集作为未来研究的新基准。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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