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Mamba and Vision Mamba (Vim) models have shown their potential as an alternative to methods based on Transformer architecture.
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
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Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., Ré, C.: Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems 34
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Rao, Y., Zhao, W., Liu, B., Lu, J., Zhou, J., Hsieh, C.J.: Dynamicvit: Efficient vision transformers with dynamic token sparsification. Advances in neural information processing systems 34
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Zhu, M., Tang, Y., Han, K.: Vision transformer pruning. arXiv preprint arXiv:2104.08500 (2021)
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Bolya, D., Fu, C.Y., Dai, X., Zhang, P., Feichtenhofer, C., Hoffman, J.: Token merging: Your vit but faster. In: The Eleventh International Conference on Learning Representations (2022)
2022
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Ding, Y., Qin, H., Yan, Q., Chai, Z., Liu, J., Wei, X., Liu, X.: Towards accurate post-training quantization for vision transformer. In: Proceedings of the 30th ACM international conference on multimedia. pp. 5380–5388 (2022)
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Gupta, A., Gu, A., Berant, J.: Diagonal state spaces are as effective as structured state spaces. Advances in Neural Information Processing Systems (2022)
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Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Shen, X., Yuan, G., Ren, B., Tang, H., et al.: Spvit: Enabling faster vision transformers via latency-aware soft token pruning. In: European conference on computer vision. pp. 620–640. Springer (2022)
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Meng, L., Li, H., Chen, B.C., Lan, S., Wu, Z., Jiang, Y.G., Lim, S.N.: Adavit: Adaptive vision transformers for efficient image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12309–12318 (2022)
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Tang, Y., Han, K., Wang, Y., Xu, C., Guo, J., Xu, C., Tao, D.: Patch slimming for efficient vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12165–12174 (2022)
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Yin, H., Vahdat, A., Alvarez, J.M., Mallya, A., Kautz, J., Molchanov, P.: A-vit: Adaptive tokens for efficient vision transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10809–10818 (2022)
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Yuan, Z., Xue, C., Chen, Y., Wu, Q., Sun, G.: Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization. In: European conference on computer vision. pp. 191–207. Springer (2022)
2024
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2022
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2023
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2023
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Liu, Y., Yang, H., Dong, Z., Keutzer, K., Du, L., Zhang, S.: Noisyquant: Noisy bias-enhanced post-training activation quantization for vision transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20321–20330 (2023)
2023
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Mehta, H., Gupta, A., Cutkosky, A., Neyshabur, B.: Long range language modeling via gated state spaces. In: International Conference on Learning Representations (2023)
2023
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2023
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Wang, J., Zhu, W., Wang, P., Yu, X., Liu, L., Omar, M., Hamid, R.: Selective structured state-spaces for long-form video understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6387–6397 (2023)
2023
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Yang, H., Yin, H., Shen, M., Molchanov, P., Li, H., Kautz, J.: Global vision transformer pruning with hessian-aware saliency. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 18547–18557 (2023)
2023
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2024
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Zhang, Y., Liu, Y., Miao, D., Zhang, Q., Shi, Y., Hu, L.: Mg-vit: a multi-granularity method for compact and efficient vision transformers. Advances in Neural Information Processing Systems 36
2024
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