2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDIVMSP-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
SessionIVMSP-10: Metric Learning and Interpretability
LocationGather.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
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.