Search results
Results: 297
Number of items: 297
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Shi, Z., Mettes, P., & Snoek, C. G. M. (2024). Focus for Free in Density-Based Counting. International Journal of Computer Vision, 132(7), 2600-2617. https://doi.org/10.1007/s11263-024-01990-3 -
Hu, V. T., Zhang, W., Tang, M., Mettes, P., Zhao, D., & Snoek, C. (2024). Latent Space Editing in Transformer-Based Flow Matching. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence: AAAI-2024 (Vol. 3, pp. 2247-2255). AAAI Press. https://doi.org/10.1609/aaai.v38i3.27998 -
Rastegar, S., Doughty, H., & Snoek, C. G. M. (2024). Background no more: Action recognition across domains by causal interventions. Computer Vision and Image Understanding, 242, Article 103975. https://doi.org/10.1016/j.cviu.2024.103975 -
Du, Y., Sun, H., Zhen, X., Xu, J., Yin, Y., Shao, L., & Snoek, C. G. M. (2024). MetaKernel: Learning Variational Random Features With Limited Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(3), 1464-1478. https://doi.org/10.1109/TPAMI.2022.3154930 -
Zhang, Y., Doughty, H., & Snoek, C. G. M. (2024). Low-Resource Vision Challenges for Foundation Models. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2024 : Seattle, Washington, USA, 16-22 June 2024 : proceedings (pp. 21956-21966). IEEE Computer Society. https://doi.org/10.48550/arXiv.2401.04716, https://doi.org/10.1109/CVPR52733.2024.02073 -
Du, Y., Shen, J., Zhen, X., & Snoek, C. G. M. (2023). EMO: Episodic Memory Optimization for Few-Shot Meta-Learning. Proceedings of Machine Learning Research, 232. https://doi.org/10.48550/arXiv.2306.05189 -
Zhang, Y., Zhang, D. W., Lacoste-Julien, S., Burghouts, G. J., & Snoek, C. G. M. (2023). Unlocking Slot Attention by Changing Optimal Transport Costs. Proceedings of Machine Learning Research, 202, 41931-41951. https://proceedings.mlr.press/v202/zhang23ba.html -
Bernasco, W., Hoeben, E. M., Koelma, D., Liebst, L. S., Thomas, J., Appelman, J., Snoek, C. G. M., & Lindegaard, M. R. (2023). Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing. Sociological Methods and Research, 52(3), 1239–1287. https://doi.org/10.1177/00491241221099554
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