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Information Engineering

InViTMixup: plant disease classification using convolutional vision transformer with Mixup augmentation

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Pages 520-527 | Received 13 Jul 2022, Accepted 25 Mar 2024, Published online: 10 May 2024

References

  • Ahmad, M., M. Abdullah, H. Moon, and D. Han. 2021. “Plant Disease Detection in Imbalanced Datasets Using Efficient Convolutional Neural Networks with Stepwise Transfer Learning.” IEEE Access 9:140565–140580. doi:10.1109/ACCESS.2021.3119655.
  • Chen, C. F. R., Q. Fan, and R. Panda. 2021. “Crossvit: Cross-Attention Multi-Scale Vision Transformer for Image Classification.” In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10–17 October 2021: 347–356. Washington, DC: IEEE. doi:10.1109/ICCV48922.2021.00041.
  • d’Ascoli, S., H. Touvron, M. L. Leavitt, A. S. Morcos, G. Biroli, and L. Sagun. 2022. “ConVit: Improving Vision Transformers with Soft Convolutional Inductive Biases.” Journal of Statistical Mechanics: Theory and Experiment 2022 (11): 114005. doi:10.1088/1742-5468/ac9830.
  • Guo, J., K. Han, H. Wu, Y. Tang, X. Chen, Y. Wang, and C. Xu. 2022. “CMT: Convolutional Neural Networks Meet Vision Transformers.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18–24 June 2022:12165–12175. Washington, DC: IEEE. doi:10.1109/CVPR52688.2022.01186.
  • Hansen, N., H. Su, and X. Wang. 2021. “Stabilizing Deep Q-Learning with ConvNets and Vision Transformers Under Data Augmentation.” Advances in Neural Information Processing Systems 34:3680–3693. San Diego: NeurIPS.
  • Li, Y., S. Xie, X. Chen, P. Dollar, K. He, and R. Girshick. 2021. Benchmarking Detection Transfer Learning with Vision Transformers. Cornell University. arXiV preprint. doi:10.48550/arXiv.2111.11429.
  • Lian, J., Y. Zhang, M. Fan, H. Pu, J. Lin, and Y. Zheng. 2021. “Deep Representation for Classification of Refrigerator Image via Novel Convolutional Neural Network.” Journal of the Chinese Institute of Engineers 44 (1): 33–40. doi:10.1080/02533839.2020.1831964.
  • Malpure, D., O. Litake, and R. Ingle. 2021. Investigating Transfer Learning Capabilities of Vision Transformers and CNNs by Fine-Tuning a Single Trainable Block. Cornell University. arXiV preprint. doi:10.48550/arXiv.2110.05270.
  • Radford, A., L. Metz, and S. Chintala. 2016. “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.” In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016. doi:10.48550/arXiv.1511.06434.
  • Raghu, M., T. Unterthiner, S. Kornblith, C. Zhang, and A. Dosovitskiy. 2021. “Do Vision Transformers See Like Convolutional Neural Networks?” Advances in Neural Information Processing Systems 34:12116–12128. San Diego: NeurIPS.
  • Steiner, A., A. Kolesnikov, X. Zhai, R. Wightman, J. Uszkoreit, and L. Beyer. 2021. How to Train Your ViT? Data, Augmentation, and Regularization in Vision Transformers. Cornell University. arXiV preprint. doi:10.48550/arXiv.2106.10270.
  • Sun, L., C. Xia, W. Yin, T. Liang, P. S. Yu, and L. He. 2020. Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks. Cornell University. arXiV preprint. doi:10.48550/arXiv.2010.0239.
  • Tzeng, Y. C. 2008. “Distance Weighted Multiple Classifiers Systems Applied to Remote Sensing Images Classification/Data Fusion.” Journal of the Chinese Institute of Engineers 31 (4): 639–647. doi:10.1080/02533839.2008.9671417.
  • Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. “Attention is All You Need.” In Advances in Neural Information Processing Systems 31 (NIPS 2017), 5998–6008. San Diego: NeurIPS.
  • Wu, C., F. Wu, T. Qi, Y. Huang, and X. Xie. 2021. Fastformer: Additive Attention can be all you need. Cornell University. arXiV preprint. doi:10.48550/arXiv.2108.09084.
  • Wu, H., B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang. 2021.” CvT: Introducing Convolutions to Vision Transformers.” In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10-17 October 2021: 22–31. Washington, DC: IEEE. doi:10.1109/ICCV48922.2021.00009.
  • Wu, H., and Z. Zhou. 2021. “Using Convolution Neural Network for Defective Image Classification of Industrial Components.” Mobile Information Systems 2021:1–8. doi:10.1155/2021/9092589.
  • Yang, G., G. Chen, Y. He, Z. Yan, Y. Guo, and J. Ding. 2020. “Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases.” IEEE Access 8:211912–211923. doi:10.1109/ACCESS.2020.3039345.

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