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Vision Transformers (ViTs) have emerged as state-of-the-art models for various vision tasks recently.
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2017
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2017
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Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang, “Soft filter pruning for accelerating deep convolutional neural networks,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , ser. IJCAI’18. AAAI Press, 2018, p. 2234–2240
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Z. Cai and N. Vasconcelos, “Cascade r-cnn: High quality object detection and instance segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 5, pp. 1483–1498, 2019
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Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang, “Filter pruning via geometric median for deep convolutional neural networks acceleration,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4340–4349
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=YicbFdNTTy
2021
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Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
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H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, and W. Gao, “Pre-trained image processing transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 12 299–12 310
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C. Riquelme, J. Puigcerver, B. Mustafa, M. Neumann, R. Jenatton, A. Susano Pinto, D. Keysers, and N. Houlsby, “Scaling vision with sparse mixture of experts,” Advances in Neural Information Processing Systems , vol. 34, pp. 8583–8595, 2021
2021
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T. Chen, Y. Cheng, Z. Gan, L. Yuan, L. Zhang, and Z. Wang, “Chasing sparsity in vision transformers: An end-to-end exploration,” Advances in Neural Information Processing Systems , vol. 34, pp. 19 974–19 988, 2021
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Y. Rao, W. Zhao, B. Liu, J. Lu, J. Zhou, and C.-J. Hsieh, “Dynamicvit: Efficient vision transformers with dynamic token sparsification,” Advances in neural information processing systems , vol. 34, pp. 13 937–13 949, 2021
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M. Zhu, Y. Tang, and K. Han, “Vision transformer pruning,” arXiv preprint arXiv:2104.08500 , 2021
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B. Pan, R. Panda, Y. Jiang, Z. Wang, R. Feris, and A. Oliva, “Ia-red 2 : Interpretability-aware redundancy reduction for vision transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 24 898–24 911, 2021
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L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, and S. Yan, “Tokens-to-token vit: Training vision transformers from scratch on imagenet,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 558–567
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C.-F. R. Chen, Q. Fan, and R. Panda, “Crossvit: Cross-attention multi-scale vision transformer for image classification,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 357–366
2021
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A. Ali, H. Touvron, M. Caron, P. Bojanowski, M. Douze, A. Joulin, I. Laptev, N. Neverova, G. Synnaeve, J. Verbeek et al. , “Xcit: Cross-covariance image transformers,” Advances in neural information processing systems , vol. 34, pp. 20 014–20 027, 2021
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B. Graham, A. El-Nouby, H. Touvron, P. Stock, A. Joulin, H. Jégou, and M. Douze, “Levit: a vision transformer in convnet’s clothing for faster inference,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 12 259–12 269
2021
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X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable {detr}: Deformable transformers for end-to-end object detection,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=gZ9hCDWe6ke
P. Wang, X. Wang, F. Wang, M. Lin, S. Chang, H. Li, and R. Jin, “Kvt: k-nn attention for boosting vision transformers,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXIV . Springer, 2022, pp. 285–302
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2021
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Z. Dai, B. Cai, Y. Lin, and J. Chen, “Up-detr: Unsupervised pre-training for object detection with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 1601–1610
2021
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2021
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S. He, H. Luo, P. Wang, F. Wang, H. Li, and W. Jiang, “Transreid: Transformer-based object re-identification,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 15 013–15 022
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2021
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B. Cheng, A. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,” Advances in Neural Information Processing Systems , vol. 34, pp. 17 864–17 875, 2021
2021
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Y. Wang, Z. Xu, X. Wang, C. Shen, B. Cheng, H. Shen, and H. Xia, “End-to-end video instance segmentation with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 8741–8750
2021
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2021
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2022
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J. Zhang, H. Peng, K. Wu, M. Liu, B. Xiao, J. Fu, and L. Yuan, “Minivit: Compressing vision transformers with weight multiplexing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 145–12 154
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W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, and S. Yan, “Metaformer is actually what you need for vision,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 819–10 829
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K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, Z. Yang, Y. Zhang, and D. Tao, “A survey on vision transformer,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 1, pp. 87–110, 2023
2023
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Y. Xu, J. Zhang, Q. Zhang, and D. Tao, “Vitpose++: Vision transformer for generic body pose estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
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Y. Song, Z. He, H. Qian, and X. Du, “Vision transformers for single image dehazing,” IEEE Transactions on Image Processing , vol. 32, pp. 1927–1941, 2023
2023
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