Multi-depth dilated network for fashion landmark detection with batch-level online hard keypoint mining

作者：

Highlights：

• A novel Multi-Depth Dilated (MDD)block that can efficiently extract different levels of large-scale context information, which is beneficial for the inference of hard keypoints, is proposed.

• The Batch-level Online Hard Keypoints Mining(B-OHKM)method is proposed for training to further improve the effectiveness of hard keypoints detection.

• It is demonstrated that anetwork (MDDNet) that uses the MDD block and B-OHKM training method obtains significant improvements over state-of-the-artmethodsonstandardbenchmarksforfashionlandmarkdetection.

摘要

•A novel Multi-Depth Dilated (MDD)block that can efficiently extract different levels of large-scale context information, which is beneficial for the inference of hard keypoints, is proposed.•The Batch-level Online Hard Keypoints Mining(B-OHKM)method is proposed for training to further improve the effectiveness of hard keypoints detection.•It is demonstrated that anetwork (MDDNet) that uses the MDD block and B-OHKM training method obtains significant improvements over state-of-the-artmethodsonstandardbenchmarksforfashionlandmarkdetection.

论文关键词：Fashion landmark detection,Convolutional neural network,Deep learning

论文评审过程：Received 11 January 2020, Revised 26 April 2020, Accepted 3 May 2020, Available online 15 May 2020, Version of Record 22 May 2020.

论文官网地址：https://doi.org/10.1016/j.imavis.2020.103930