| Paper ID | IVMSP-10.4 |
| Paper Title |
DISTRIBUTION-AWARE HIERARCHICAL WEIGHTING METHOD FOR DEEP METRIC LEARNING |
| Authors |
Yinong Zhu, Yong Feng, Chongqing University, China; Mingliang Zhou, University of Macau, China; Baohua Qiang, Guilin University of Electronic Technology, China; Leong Hou U, University of Macau, China; Jiajie Zhu, Chongqing University, China |
| Session | IVMSP-10: Metric Learning and Interpretability |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we propose distribution-aware hierarchical weighting (DHW) method for deep metric learning. First, we formulate the distributions of different classes according to the form of gaussian curves, and update distributions as the training process. Second, depending on the learnable distribution, we propose a loss function named distribution-aware loss with dynamic mining margins and hierarchical degrees of weights to make full use of samples. The experimental results show that our algorithm outperforms other state-of-theart methods in terms of retrieval and clustering tasks. Code is available at https://github.com/zhuyinong1/DHW-master. |
