Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification

Authors

  • Rui Yang Zhengzhou University of Science and Technology image/svg+xml
  • Dahai Li Henan Intelligent Information Processing and Control Engineering Technology Research Center

DOI:

https://doi.org/10.4108/eai.27-1-2022.173165

Keywords:

Multichannel attention mechanism, result fusion, fine-grained image classification, gate recurrent unit memory network

Abstract

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173792.

Attention mechanism is widely used in fine-grained image classification. Most of the existing methods are to construct an attention weight map for simple weighted processing of features, but there are problems of low efficiency and slow convergence. Therefore, this paper proposes a multi-channel attention fusion mechanism based on the deep neural network model which can be trained end-to-end. Firstly, the different regions corresponding to the object are described by the attention diagram. Then the corresponding higher order statistical characteristics are extracted to obtain the corresponding representation. In many standard fine-grained image classification test tasks, the proposed method works best compared with other methods.

Downloads

Published

27-01-2022

How to Cite

1.
Yang R, Li D. Multichannel attention mechanisms fusion based on gate recurrent unit memory network for fine-grained image classification. EAI Endorsed Scal Inf Syst [Internet]. 2022 Jan. 27 [cited 2024 Nov. 21];9(4):e19. Available from: https://publications.eai.eu/index.php/sis/article/view/338