Multi-Loss Weighting for Person Re-Identification Based on Deep Learning

IR IR (HANDLE) Web Site

Bibliographic Information

Other Title
  • 深層学習による人物再同定における損失関数への重み付け手法
  • シンソウ ガクシュウ ニ ヨル ジンブツ サイドウテイ ニ オケル ソンシツ カンスウ エ ノ オモミズケ シュホウ

Search this article

Description

Person re-identification is an important component to realize various image recognition systems, e.g., person tracking system by utilizing multiple cameras. Person re-identification based on deep learning has achieved high performance, and cross-entropy loss or triplet loss is generally used as the loss function. In recent years, a linear summation of both loss functions has been attracting attention as an approach to person re-identification. However, when loss functions with different properties are used at the same time, a method of synthesis by weighted linear summation that takes into account the effect of the loss function on the other loss function is necessary. To overcome above problems, in this paper, a method that automatically adjusts the weights of loss functions during learning is proposed.

Journal

  • Seikei-Kakou

    Seikei-Kakou 53 (1), 28-32, 2024

    プラスチック成形加工学会

Keywords

Details 詳細情報について

Report a problem

Back to top