| SciPy 1.0: fundamental algorithms for scientific computing in Python P Virtanen, R Gommers, TE Oliphant, M Haberland, T Reddy, ... Nature methods 17 (3), 261-272, 2020 | 34475 | 2020 |
| Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet M Kümmerer, L Theis, M Bethge arXiv preprint arXiv:1411.1045, 2014 | 522 | 2014 |
| Understanding low-and high-level contributions to fixation prediction M Kummerer, TSA Wallis, LA Gatys, M Bethge Proceedings of the IEEE international conference on computer vision, 4789-4798, 2017 | 365 | 2017 |
| DeepGaze II: Reading fixations from deep features trained on object recognition M Kümmerer, TSA Wallis, M Bethge arXiv preprint arXiv:1610.01563, 2016 | 349 | 2016 |
| Information-theoretic model comparison unifies saliency metrics M Kümmerer, TSA Wallis, M Bethge Proceedings of the National Academy of Sciences 112 (52), 16054-16059, 2015 | 193 | 2015 |
| Accurate, reliable and fast robustness evaluation W Brendel, J Rauber, M Kümmerer, I Ustyuzhaninov, M Bethge Advances in neural information processing systems 32, 2019 | 140 | 2019 |
| Saliency benchmarking made easy: Separating models, maps and metrics M Kummerer, TSA Wallis, M Bethge Proceedings of the European Conference on Computer Vision (ECCV), 770-787, 2018 | 134 | 2018 |
| DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling A Linardos, M Kümmerer, O Press, M Bethge Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 84 | 2021 |
| DeepGaze III: Modeling free-viewing human scanpaths with deep learning M Kümmerer, M Bethge, TSA Wallis Journal of Vision 22 (5), 7-7, 2022 | 74 | 2022 |
| Mit/tübingen saliency benchmark M Kümmerer, Z Bylinskii, T Judd, A Borji, L Itti, F Durand, A Oliva, ... Tübingen saliency benchmark, 2020 | 53 | 2020 |
| State-of-the-art in human scanpath prediction M Kümmerer, M Bethge arXiv preprint arXiv:2102.12239, 2021 | 39 | 2021 |
| Attention to comics: Cognitive processing during the reading of graphic literature J Laubrock, S Hohenstein, M Kümmerer Empirical comics research, 239-263, 2018 | 37 | 2018 |
| Rdumb: A simple approach that questions our progress in continual test-time adaptation O Press, S Schneider, M Kümmerer, M Bethge Advances in Neural Information Processing Systems 36, 2024 | 30 | 2024 |
| Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations MA Pedziwiatr, M Kümmerer, TSA Wallis, M Bethge, C Teufel Cognition 206, 104465, 2021 | 30 | 2021 |
| Unsupervised object learning via common fate M Tangemann, S Schneider, J Von Kügelgen, F Locatello, P Gehler, ... arXiv preprint arXiv:2110.06562, 2021 | 26 | 2021 |
| Deepgaze ii: Predicting fixations from deep features over time and tasks M Kümmerer, T Wallis, M Bethge Journal of Vision 17 (10), 1147-1147, 2017 | 21 | 2017 |
| Guiding human gaze with convolutional neural networks LA Gatys, M Kümmerer, TSA Wallis, M Bethge arXiv preprint arXiv:1712.06492, 2017 | 19 | 2017 |
| Predicting visual fixations M Kümmerer, M Bethge Annual Review of Vision Science 9 (1), 269-291, 2023 | 15 | 2023 |
| How close are we to understanding image-based saliency? M Kümmerer, T Wallis, M Bethge arXiv preprint arXiv:1409.7686, 2014 | 14 | 2014 |
| Measuring the importance of temporal features in video saliency M Tangemann, M Kümmerer, TSA Wallis, M Bethge Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 13 | 2020 |
Matthias BethgeTübingen University & Maddox Co-FounderEmail confirmado em bethgelab.org