Deep learning has been applied to fashion landmark detection in recent years, and great progress has been made. However, the detection of hard keypoints, such as those which are occluded or invisible, remains challenging and must be addressed. To tackle this problem, in the feature exaction level a novel Multi-Depth Dilated (MDD) block which is composed of different numbers of dilated convolutions in parallel and a Multi-Depth Dilated Network (MDDNet) constructed by MDD blocks are proposed in this paper, and in the training level a network training method of Batch-level Online Hard Keypoint Mining (B-OHKM) is proposed. During the training of network, each clothing keypoint is one-to-one corresponding to the related loss value calculated at that keypoint. The greater the loss of the keypoint, the more difficult it is for the network to detect that keypoint. In that way, hard keypoints can be effectively mined, so that the network can be trained in a targeted manner to improve the performance of hard keypoints. The results of experiments on two large-scale fashion benchmark datasets demonstrate that the proposed MDDNet that uses the MDD block and B-OHKM method achieves state-of-the-art results.

近年来，深度学习已应用于时尚界标检测，并且已经取得了很大的进步。但是，检测硬关键点（例如被遮挡或看不见的关键点）仍然具有挑战性，必须加以解决。为了解决这个问题，本文提出了一种在特征精确度上新颖的由不同数量的卷积卷积组成的新型MDD块，并提出了一种由MDD块构成的MDDNet网络。 ，在训练层次上，提出了一种基于批处理的在线硬关键点挖掘（B-OHKM）网络训练方法。在网络训练期间，每个服装关键点是一对一的，对应于在该关键点计算的相关损耗值。关键点的损失越大，网络检测该关键点越困难。这样，可以有效地挖掘硬性关键点，从而可以有针对性地训练网络以提高硬性关键点的性能。在两个大型时尚基准数据集上的实验结果表明，使用MDD块和B-OHKM方法的拟议MDDNet达到了最新水平。