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Transformers have recently gained significant attention in the computer vision community.
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Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4510–4520 (2018)
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d’Ascoli, S., Touvron, H., Leavitt, M.L., Morcos, A.S., Biroli, G., Sagun, L.: Convit: Improving vision transformers with soft convolutional inductive biases. In: International Conference on Machine Learning. pp. 2286–2296. PMLR (2021)
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Chen, T., Zhai, X., Ritter, M., Lucic, M., Houlsby, N.: Self-supervised gans via auxiliary rotation loss. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12154–12163 (2019)
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Fang, H.S., Sun, J., Wang, R., Gou, M., Li, Y.L., Lu, C.: Instaboost: Boosting instance segmentation via probability map guided copy-pasting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 682–691 (2019)
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Jiang, Y., Chang, S., Wang, Z.: Transgan: Two pure transformers can make one strong gan, and that can scale up. Advances in Neural Information Processing Systems 34
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