[59] Z. Shao, X. Zhang, M. Fang, Z. Lin, J. Wang, and C. Ding. Learning granularity-unified representations for text-to-image person re-identification. In Proceedings of the 30th ACM International Conference on Multimedia, pages 5566–5574, 2022.
[60] C. Su, J. Li, S. Zhang, J. Xing, W. Gao, and Q. Tian. Pose-driven deep convolutional model for person re-identification. In Proceedings of the IEEE international conference on computer vision, pages 3960–3969, 2017.
[61] K. Sun, B. Xiao, D. Liu, and J. Wang. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693-5703, 2019.
[62] W. Suo, M. Sun, K. Niu, Y. Gao, P. Wang, Y. Zhang, and Q. Wu. A simple and robust correlation filtering method for text-based person search. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXV, pages 726-742. Springer, 2022.
[63] C. Tang, L. Sheng, Z. Zhang, and X. Hu. Improving pedestrian attribute recognition with weakly-supervised multi-scale attribute-specific localization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4997-5006, 2019.
[64] S. Tang, C. Chen, Q. Xie, M. Chen, Y. Wang, Y. Ci, L. Bai, F. Zhu, H. Yang, L. Yi, et al. Humanbench: Towards general human-centric perception with projector assisted pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
[65] Z. Tong, Y. Song, J. Wang, and L. Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. arXiv preprint arXiv:2203.12602, 2022.
[66] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[67] T. Von Marcard, R. Henschel, M. J. Black, B. Rosenhahn, and G. Pons-Moll. Recovering accurate 3d human pose in the wild using imus and a moving camera. In Proceedings of the European conference on computer vision (ECCV), pages 601-617, 2018.
[68] H. Wang, K. Song, J. Fan, Y. Wang, J. Xie, and Z. Zhang. Hard patches mining for masked image modeling. arXiv preprint arXiv:2304.05919, 2023.
[69] X. Wang, R. Zhang, C. Shen, T. Kong, and L. Li. Dense contrastive learning for self-supervised visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3024-3033, 2021.
[70] Z. Wang, A. Zhu, J. Xue, X. Wan, C. Liu, T. Wang, and Y. Li. Caibc: Capturing all-round information beyond color for text-based person retrieval. In Proceedings of the 30th ACM International Conference on Multimedia, pages 5314-5322, 2022.
[71] Z. Wang, A. Zhu, J. Xue, X. Wan, C. Liu, T. Wang, and Y. Li. Look before you leap: Improving text-based person retrieval by learning a consistent cross-modal common manifold. In Proceedings of the 30th ACM International Conference on Multimedia, pages 1984-1992, 2022.
[72] C. Wei, H. Fan, S. Xie, C.-Y. Wu, A. Yuille, and C. Feichtenhofer. Masked feature prediction for self-supervised visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14668-14678, 2022.
[73] L. Wei, L. Xie, W. Zhou, H. Li, and Q. Tian. Mvp: Multimodality-guided visual pre-training. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXX, pages 337-353. Springer, 2022.
[74] L. Wei, S. Zhang, W. Gao, and Q. Tian. Person transfer gan to bridge domain gap for person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 79–88, 2018.
[75] J. Wu, H. Zheng, B. Zhao, Y. Li, B. Yan, R. Liang, W. Wang, S. Zhou, G. Lin, Y. Fu, et al. Ai challenger: A large-scale dataset for going deeper in image understanding. arXiv preprint arXiv:1711.06475, 2017.
[76] B. Xiao, H. Wu, and Y. Wei. Simple baselines for human pose estimation and tracking. In Proceedings of the European conference on computer vision (ECCV), pages 466-481, 2018.
[77] Z. Xie, Z. Zhang, Y. Cao, Y. Lin, J. Bao, Z. Yao, Q. Dai, and H. Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9653-9663, 2022.