| Ood-cv: A benchmark for robustness to out-of-distribution shifts of individual nuisances in natural images B Zhao, S Yu, W Ma, M Yu, S Mei, A Wang, J He, A Yuille, A Kortylewski European conference on computer vision, 163-180, 2022 | 41* | 2022 |
| The 1st agriculture-vision challenge: Methods and results MT Chiu, X Xu, K Wang, J Hobbs, N Hovakimyan, TS Huang, H Shi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 30 | 2020 |
| Rethinking out-of-distribution (ood) detection: Masked image modeling is all you need J Li, P Chen, Z He, S Yu, S Liu, J Jia Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 18 | 2023 |
| Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization S Yang, S Yu, B Zhao, Y Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 13 | 2020 |
| Rail-5k: A real-world dataset for rail surface defects detection Z Zhang, S Yu, S Yang, Y Zhou, B Zhao arXiv preprint arXiv:2106.14366, 2021 | 8 | 2021 |
| Ood-cv-v2: An extended benchmark for robustness to out-of-distribution shifts of individual nuisances in natural images B Zhao, J Wang, W Ma, A Jesslen, S Yang, S Yu, O Zendel, C Theobalt, ... arXiv preprint arXiv:2304.10266, 2023 | 3 | 2023 |
| Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning Y Wang, S Yu, X Yang, W Shen arXiv preprint arXiv:2106.02824, 2021 | 1 | 2021 |
| MOODv2: Masked Image Modeling for Out-of-Distribution Detection J Li, P Chen, S Yu, S Liu, J Jia arXiv preprint arXiv:2401.02611, 2024 | | 2024 |
| BAL: Balancing Diversity and Novelty for Active Learning J Li, P Chen, S Yu, S Liu, J Jia IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | | 2023 |
Adam KortylewskiResearch Group Leader, University of Freiburg and Max Planck Institute for InformaticsVerified email at cs.uni-freiburg.de
