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Results: 19
Number of items: 19
  • Open Access
    Du, Y., Shen, J., Zhen, X., & Snoek, C. G. M. (2023). SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail. In CVPR 2023: proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition : Vancouver, Canada : 18-22 June 2023 (pp. 19944-19954). IEEE Computer Society. https://doi.org/10.48550/arXiv.2304.00101, https://doi.org/10.1109/CVPR52729.2023.01910
  • Open Access
    Chen, S., Du, Y., Mettes, P., & Snoek, C. G. M. (2023). Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation. In ICMR'23: proceedings of the 2023 ACM International Conference on Multimedia Retrieval : Thessaloniki, Greece, June 12-15, 2023 (pp. 39-47). Association for Computing Machinery. https://doi.org/10.48550/arXiv.2306.10122, https://doi.org/10.1145/3591106.3592267
  • Li, J., Huang, Q., Du, Y., Zhen, X., Chen, S., & Shao, L. (2022). Variational Abnormal Behavior Detection With Motion Consistency. IEEE Transactions on Image Processing, 31, 275-286. https://doi.org/10.1109/TIP.2021.3130545
  • Open Access
    Du, Y., Zhen, X., Shao, L., & Snoek, C. G. M. (2022). Hierarchical Variational Memory for Few-shot Learning Across Domains. In The Tenth International Conference on Learning Representations: ICLR 2022 https://openreview.net/forum?id=i3RI65sR7N
  • Zhang, L., Zuo, L., Du, Y., & Zhen, X. (2021). Learning to Adapt with Memory for Probabilistic Few-Shot Learning. IEEE transactions on circuits and systems for video technology, 31(11), 4283-4292. https://doi.org/10.1109/TCSVT.2021.3052785
  • Open Access
    Du, Y., Holla, N., Zhen, X., Snoek, C. G. M., & Shutova, E. (2021). Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 5254-5268). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.409
  • Open Access
    Zhen, X., Du, Y., Xiong, H., Qiu, Q., Snoek, C., & Shao, L. (2021). Learning to Learn Variational Semantic Memory. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 11, pp. 9122-9134). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/67d16d00201083a2b118dd5128dd6f59-Abstract.html
  • 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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