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Results: 37
Number of items: 37
  • Open Access
    Wang, J., Tan, S., Zhen, X., Xu, S., Zheng, F., He, Z., & Shao, L. (2021). Deep 3D human pose estimation: A review. Computer Vision and Image Understanding, 210, Article 103225. https://doi.org/10.1016/j.cviu.2021.103225
  • Open Access
    Chen, H., Wang, J., Chen, H. C., Zhen, X., Zheng, F., Ji, R., & Shao, L. (2021). Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation. In 2021 IEEE/CVF International Conference on Computer Vision: proceedings : ICCV 2021 : 11-17 October 2021, virtual event (pp. 6900-6909). (International Conference on Computer Vision; Vol. 18). IEEE Computer Society. https://doi.org/10.1109/ICCV48922.2021.00684
  • Open Access
    Wang, H., Yang, Y., Cao, X., Zhen, X., Snoek, C., & Shao, L. (2021). Variational prototype inference for few-shot semantic segmentation. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 525-534). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00057
  • Qi, M., Qin, J., Zhen, X., Huang, D., Yang, Y., & Luo, J. (2020). Few-Shot Ensemble Learning for Video Classification with SlowFast Memory Networks. In MM '20: proceedings of the 28th ACM International Conference on Multimedia : October 12-16, 2020, Virtual Event, USA (pp. 3007-3015). Association for Computing Machinery. https://doi.org/10.1145/3394171.3416269
  • Wang, H., Zhang, X., Hu, Y., Hu, Y., Yang, Y., Cao, X., & Zhen, X. (2020). Few-Shot Semantic Segmentation with Democratic Attention Networks. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings (Vol. XIII, pp. 730-746). (Lecture Notes in Computer Science; Vol. 12358). Springer. https://doi.org/10.1007/978-3-030-58601-0_43
  • Open Access
    Zhen, X., Sun, H., Du, Y., Xu, J., Yin, Y., Shao, L., & Snoek, C. (2020). Learning to learn kernels with variational random features. Proceedings of Machine Learning Research, 119, 11409-11419. https://doi.org/10.48550/arXiv.2006.06707
  • Open Access
    Du, Y., Xu, J., Xiong, H., Qiu, Q., Zhen, X., Snoek, C. G. M., & Shao, L. (2020). Learning to Learn with Variational Information Bottleneck for Domain Generalization. In A. Vedaldi, H. Bischof, T. Brox, & J. M. Frahm (Eds.), Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020 : proceedings (Vol. X, pp. 200-216). (Lecture Notes in Computer Science; Vol. 12355). Springer. https://doi.org/10.1007/978-3-030-58607-2_12
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