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In recent years, Transformers have become the de-facto architecture for sequence modeling on text and a variety of multi-dimensional data, such as images and video.
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Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6202–6211 (2019)
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Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: Vivit: A video vision transformer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6836–6846 (2021)
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Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML. vol. 2, p. 4 (2021)
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Dai, Z., Liu, H., Le, Q.V., Tan, M.: Coatnet: Marrying convolution and attention for all data sizes. Advances in neural information processing systems 34
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Mangalam, K., Fan, H., Li, Y., Wu, C.Y., Xiong, B., Feichtenhofer, C., Malik, J.: Reversible Vision Transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10830–10840 (2022)
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Nguyen, E., Goel, K., Gu, A., Downs, G.W., Shah, P., Dao, T., Baccus, S.A., Ré, C.: S4nd: Modeling images and videos as multidimensional signals using state spaces. Advances in Neural Information Processing Systems 35
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Zhang, X., Tian, Y., Xie, L., Huang, W., Dai, Q., Ye, Q., Tian, Q.: Hivit: A simpler and more efficient design of hierarchical vision transformer. In: The Eleventh International Conference on Learning Representations (2022)
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2023
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Gu, A., Dao, T.: Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Dec 2023). https://doi.org/10.48550/arXiv.2312.00752
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Islam, M.M., Hasan, M., Athrey, K.S., Braskich, T., Bertasius, G.: Efficient movie scene detection using state-space transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18749–18758 (2023)
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2023
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2023
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Tian, Y., Xie, L., Wang, Z., Wei, L., Zhang, X., Jiao, J., Wang, Y., Tian, Q., Ye, Q.: Integrally pre-trained transformer pyramid networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18610–18620 (2023)
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2024
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2024
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Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., Wang, X.: Vision mamba: Efficient visual representation learning with bidirectional state space model (2024)
2024
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