[1] Luo J H, Wu J X. A survey on fine-grained image categorization using deep convolutional features[J]. Acta Automatica Sinica, 43, 1306-1318(2017).
[3] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).
[4] Zhang X Y, Zhou X Y, Lin M X et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).
[5] Li X, Wang W H, Hu X L et al. Selective kernel networks[C], 510-519(2019).
[6] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C], 5987-5995(2017).
[7] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[8] Han K, Wang Y H, Tian Q et al. GhostNet: more features from cheap operations[C], 1577-1586(2020).
[10] Ji R Y, Wen L Y, Zhang L B et al. Attention convolutional binary neural tree for fine-grained visual categorization[C], 10465-10474(2020).
[11] Chen J Y, Chen Y. Saliency enhanced hierarchical bilinear pooling for fine-grained classification[J]. Journal of Computer-Aided Design & Computer Graphics, 33, 241-249(2021).
[12] Bai Y Y, Liu N Z, Jiang X T. Fine grained image classification network combined with attention CutMix[J]. Computer Technology and Development, 31, 38-42(2021).
[14] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).
[15] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2018).
[16] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).
[17] Radosavovic I, Kosaraju R P, Girshick R et al. Designing network design spaces[C], 10425-10433(2020).
[18] Selvaraju R R, Cogswell M, Das A et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[J]. International Journal of Computer Vision, 128, 336-359(2020).