Search results
Results: 40
Number of items: 40
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Mensink, T., & Mettes, P. (2023). Infinite Class Mixup. In The 34th British Machine Vision Conference Proceedings: BMVC 2023 : 20th-24th November 2023, Aberdeen, UK Article 135 BMVA Press. https://doi.org/10.48550/arXiv.2305.10293 -
Lê, H.-A., Mensink, T., Das, P., Karaoglu, S., & Gevers, T. (2021). EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 1578-1588). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00162
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Groenendijk, R., Karaoglu, S., Gevers, T., & Mensink, T. (2021). Multi-Loss Weighting with Coefficient of Variations. In 2021 IEEE Winter Conference on Applications of Computer Vision: proceedings : 5-9 January 2021, virtual event (pp. 1468-1477). (WACV). IEEE Computer Society. https://doi.org/10.1109/WACV48630.2021.00151
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Lê, H. .-Â., Nimbhorkar, T., Mensink, T., Baslamisli, A. S., Karaoglu, S., & Gevers, T. (2021). Automatic generation of dense non-rigid optical flow. Computer Vision and Image Understanding, 212, Article 103274. https://doi.org/10.1016/j.cviu.2021.103274 -
Chen, Y., Fernando, B., Bilen, H., Mensink, T., & Gavves, E. (2021). Neural Feature Matching in Implicit 3D Representations. Proceedings of Machine Learning Research, 139, 1582-1593. https://proceedings.mlr.press/v139/chen21f.html -
Groenendijk, R., Karaoglu, S., Gevers, T., & Mensink, T. (2020). On the benefit of adversarial training for monocular depth estimation. Computer Vision and Image Understanding, 190, Article 102848. https://doi.org/10.1016/j.cviu.2019.102848 -
Chen, Y., Hu, V. T., Gavves, E., Mensink, T., Mettes, P., Yang, P., & Snoek, C. G. M. (2020). PointMixup: Augmentation for Point Clouds. 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. III, pp. 330-345). (Lecture Notes in Computer Science; Vol. 12348). Springer. https://doi.org/10.1007/978-3-030-58580-8_20
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