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Rank-in-Rank Loss for Person Re-identification

Published:06 October 2022Publication History
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Abstract

Person re-identification (re-ID) is commonly investigated as a ranking problem. However, the performance of existing re-ID models drops dramatically, when they encounter extreme positive-negative class imbalance (e.g., very small ratio of positive and negative samples) during training. To alleviate this problem, this article designs a rank-in-rank loss to optimize the distribution of feature embeddings. Specifically, we propose a Differentiable Retrieval-Sort Loss (DRSL) to optimize the re-ID model by ranking each positive sample ahead of the negative samples according to the distance and sorting the positive samples according to the angle (e.g., similarity score). The key idea of the proposed DRSL lies in minimizing the distance between samples of the same category along with the angle between them. Considering that the ranking and sorting operations are non-differentiable and non-convex, the DRSL also performs the optimization of automatic derivation and backpropagation. In addition, the analysis of the proposed DRSL is provided to illustrate that the DRSL not only maintains the inter-class distance distribution but also preserves the intra-class similarity structure in terms of angle constraints. Extensive experimental results indicate that the proposed DRSL can improve the performance of the state-of-the-art re-ID models, thus demonstrating its effectiveness and superiority in the re-ID task.

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      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 2s
      June 2022
      383 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3561949
      • Editor:
      • Abdulmotaleb El Saddik
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      Publication History

      • Published: 6 October 2022
      • Online AM: 30 April 2022
      • Accepted: 19 April 2022
      • Revised: 23 February 2022
      • Received: 29 October 2021
      Published in tomm Volume 18, Issue 2s

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