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Collaborative Representation for Deep Meta Metric Learning

Published: 01 September 2021 Publication History

Abstract

Most metric learning methods utilize all training data to construct a single metric, and it is usually over-fitting on the "salient" feature. To overcome this issue, we propose a deep meta metric learning method based on collaborative representation. We construct multiple episodes from the original training data to train a general metric, where each episode consists of a query set and a support set. Then, we introduce a collaborative representation method, which fits the query sample with the support samples per class. We predict the query sample's label via the optimal fitness among the query sample and the support samples in each specific class. Besides, we adopt a hard mining strategy to learn a more discriminative metric according to increasing the training tasks' difficulty. Experiments verify that our method achieves state-of-the-art results on three re-ID benchmark datasets.

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Cited By

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  • (2024)Heterogeneous Graph Fusion Network for cross-modal image-text retrievalExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123842249:PCOnline publication date: 17-Jul-2024

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cover image ACM Conferences
ICMR '21: Proceedings of the 2021 International Conference on Multimedia Retrieval
August 2021
715 pages
ISBN:9781450384636
DOI:10.1145/3460426
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 September 2021

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Author Tags

  1. collaborative representation
  2. hard mining
  3. meta learning
  4. metric learning

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  • Short-paper

Funding Sources

  • Fundamental Research Funds for the Central Universities China University of Petroleum (East China)
  • Natural Science Foundation of Shandong Province China
  • Major Scientific and Technological Projects of CNPC
  • Creative Research Team of Young Scholars at Universities in Shandong Province

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ICMR '21
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Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

View all
  • (2024)Heterogeneous Graph Fusion Network for cross-modal image-text retrievalExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123842249:PCOnline publication date: 17-Jul-2024

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