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Results: 15
Number of items: 15
  • Open Access
    Liu, J., Shen, J., Zhou, P., Sonke, J.-J., & Gavves, E. (2025). Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation. In 2025 IEEE/CVF International Conference on Computer Vision: ICCV 2025 : Honolulu, Hawaii, USA, 19-23 October 2025 : proceedings (pp. 21155-21165). IEEE Computer Society. https://doi.org/10.48550/arXiv.2506.22979, https://doi.org/10.1109/ICCV51701.2025.01965
  • Open Access
    Xiao, Z., Yan, S., Hong, J., Cai, J., Jiang, X., Hu, Y., Shen, J., Wang, Q., & Snoek, C. G. M. (2025). DynaPrompt: Dynamic Test-Time Prompt Tuning. In The Thirteenth International Conference on Learning Representations: ICLR 2025 https://openreview.net/forum?id=EFZEdHB3Mp
  • Open Access
    Liu, J., Zhou, P., Xiao, Z., Shen, J., Yin, W., Sonke, J.-J., & Gavves, E. (2025). Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes. Proceedings of Machine Learning Research, 267, 40194-40213. https://openreview.net/forum?id=6qNbVtKGY2
  • Open Access
    Shen, J., Wang, Q., Xiao, Z., Van Noord, N., & Worring, M. (2025). GO4Align: Group Optimization for Multi-Task Alignment. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 111382-111405). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-3537
  • Open Access
    Ambekar, S., Xiao, Z., Shen, J., Zhen, X., & Snoek, C. G. M. (2024). Probabilistic Test-Time Generalization by Variational Neighbor-Labeling. Proceedings of Machine Learning Research, 274, 832-851. https://proceedings.mlr.press/v274/ambekar25a.html
  • Open Access
    Wang, S., Shen, J., Efthymiou, A., Rudinac, S., Kackovic, M., Wijnberg, N., & Worring, M. (2024). Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks. In S. Rudinac, A. Hanjalic, C. Liem, M. Worring, B. Þ. Jónsson, B. Liu, & Y. Yamakata (Eds.), MultiMedia Modeling: 30th International Conference, MMM 2024, Amsterdam, The Netherlands, January 29–February 2, 2024 : proceedings (Vol. III, pp. 462-476). (Lecture Notes in Computer Science; Vol. 14556). Springer. https://doi.org/10.1007/978-3-031-53311-2_34
  • Open Access
    Shen, J. (2024). Mitigating bias in multi-task learning. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Xiao, Z., Shen, J., Derakhshani, M. M., Liao, S., & Snoek, C. G. M. (2024). Any-Shift Prompting for Generalization over Distributions. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 13849-13860). IEEE Computer Society. https://doi.org/10.48550/arXiv.2402.10099, https://doi.org/10.1109/CVPR52733.2024.01314
  • 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
  • Zhang, L., Du, Y., Shen, J., & Zhen, X. (2023). Learning to Learn With Variational Inference for Cross-Domain Image Classification. IEEE Transactions on Multimedia, 25, 3319-3328. https://doi.org/10.1109/TMM.2022.3158072
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