The Comparison of Attention Mechanisms with Different Embedding Modes for Performance Improvement of Fine-Grained Classification
Author:
Affiliation:
1. School of Integrated Circuits, Guangdong University of Technology
2. Department of Information Engineering and Computer Science, Feng Chia University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E106.D/5/E106.D_2022DLP0006/_pdf
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3. [3] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2818-2826, 2016. 10.1109/cvpr.2016.308
4. [4] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” arXiv preprint arXiv:1602.07261, pp.1-12, 2016.
5. [5] H. Jie, S. Li, S. Gang, and S. Albanie, “Squeeze-and-excitation networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.7132-7141, 2017.
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