| Flownet: Learning optical flow with convolutional networks A Dosovitskiy, P Fischer, E Ilg, P Hausser, C Hazirbas, V Golkov, ... International Conference on Computer Vision (ICCV), 2758-2766, 2015 | 5511* | 2015 |
| FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture C Hazirbas, L Ma, C Domokos, D Cremers Asian Conference on Computer Vision (ACCV), 2016 | 971 | 2016 |
| Image-based localization using LSTMs for structured feature correlation F Walch, C Hazirbas, L Leal-Taixé, T Sattler, S Hilsenbeck, D Cremers International Conference on Computer Vision (ICCV), 2017 | 670 | 2017 |
| Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems T Meinhardt, M Möller, C Hazirbas, D Cremers International Conference on Computer Vision (ICCV), 2017 | 438 | 2017 |
| What makes good synthetic training data for learning disparity and optical flow estimation? N Mayer, E Ilg, P Fischer, C Hazirbas, D Cremers, A Dosovitskiy, T Brox International Journal of Computer Vision (IJCV), 1-19, 2018 | 255 | 2018 |
| Towards measuring fairness in ai: the casual conversations dataset C Hazirbas, J Bitton, B Dolhansky, J Pan, A Gordo, CC Ferrer IEEE Transactions on Biometrics, Behavior, and Identity Science 4 (3), 324-332, 2021 | 173* | 2021 |
| CAPTCHA Recognition with Active Deep Learning F Stark, C Hazirbas, R Triebel, D Cremers German Conference on Pattern Recognition Workshop (GCPRW), 94, 2015 | 147 | 2015 |
| Deep depth from focus C Hazirbas, SG Soyer, MC Staab, L Leal-Taixé, D Cremers Asian Conference on Computer Vision (ACCV), 2018 | 111 | 2018 |
| Generating high fidelity data from low-density regions using diffusion models V Sehwag, C Hazirbas, A Gordo, F Ozgenel, C Canton Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 68 | 2022 |
| A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others Z Li, I Evtimov, A Gordo, C Hazirbas, T Hassner, CC Ferrer, C Xu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 64 | 2023 |
| Imagenet-x: Understanding model mistakes with factor of variation annotations BY Idrissi, D Bouchacourt, R Balestriero, I Evtimov, C Hazirbas, N Ballas, ... arXiv preprint arXiv:2211.01866, 2022 | 49 | 2022 |
| The casual conversations v2 dataset B Porgali, V Albiero, J Ryda, CC Ferrer, C Hazirbas Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 34* | 2023 |
| Fairness indicators for systematic assessments of visual feature extractors P Goyal, AR Soriano, C Hazirbas, L Sagun, N Usunier Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 34 | 2022 |
| Interactive Multi-label Segmentation of RGB-D Images J Diebold, N Demmel, C Hazirbas, M Möller, D Cremers Scale Space and Variational Methods in Computer Vision (SSVM), 294-306, 2015 | 20 | 2015 |
| Unibench: Visual reasoning requires rethinking vision-language beyond scaling H Al-Tahan, Q Garrido, R Balestriero, D Bouchacourt, C Hazirbas, ... arXiv preprint arXiv:2408.04810, 2024 | 12 | 2024 |
| Pinpointing why object recognition performance degrades across income levels and geographies L Gustafson, M Richards, M Hall, C Hazirbas, D Bouchacourt, M Ibrahim arXiv preprint arXiv:2304.05391, 2023 | 9* | 2023 |
| Deep Learning for Image-Based Localization F Walch, D Cremers, S Hilsenbeck, C Hazirbas, L Leal-Taixé Master’s thesis, 2016 | 9 | 2016 |
| Vpa: Fully test-time visual prompt adaptation J Sun, M Ibrahim, M Hall, I Evtimov, ZM Mao, CC Ferrer, C Hazirbas Proceedings of the 31st ACM International Conference on Multimedia, 5796-5806, 2023 | 7 | 2023 |
| Optimizing the Relevance-Redundancy Tradeoff for Efficient Semantic Segmentation C Hazirbas, J Diebold, D Cremers Scale Space and Variational Methods in Computer Vision (SSVM) 9087, 243-255, 2015 | 5* | 2015 |
| Image processing using a convolutional neural network C Schroers, F Perazzi, C Hazirbas US Patent 10,706,503, 2020 | 3 | 2020 |
Daniel CremersTechnical University of MunichE-mailová adresa ověřena na: tum.de
