| 3d morphable face models—past, present, and future B Egger, WAP Smith, A Tewari, S Wuhrer, M Zollhoefer, T Beeler, ... ACM Transactions on Graphics (TOG) 39 (5), 1-38, 2020 | 210 | 2020 |
| TransFG: A Transformer Architecture for Fine-grained Recognition J He, JN Chen, S Liu, A Kortylewski, C Yang, Y Bai, C Wang, A Yuille Proceedings of the AAAI Conference on Artificial Intelligence 36, 2022 | 101 | 2022 |
| Analyzing and reducing the damage of dataset bias to face recognition with synthetic data A Kortylewski, B Egger, A Schneider, T Gerig, A Morel-Forster, T Vetter Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 75 | 2019 |
| Occlusion-aware 3d morphable models and an illumination prior for face image analysis B Egger, S Schönborn, A Schneider, A Kortylewski, A Morel-Forster, ... International Journal of Computer Vision 126 (12), 1269-1287, 2018 | 63 | 2018 |
| Empirically analyzing the effect of dataset biases on deep face recognition systems A Kortylewski, B Egger, A Schneider, T Gerig, A Morel-Forster, T Vetter Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 55 | 2018 |
| Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion A Kortylewski, J He, Q Liu, A Yuille Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020 | 52 | 2020 |
| Training deep face recognition systems with synthetic data A Kortylewski, A Schneider, T Gerig, B Egger, A Morel-Forster, T Vetter arXiv preprint arXiv:1802.05891, 2018 | 52 | 2018 |
| Robust Object Detection under Occlusion with Context-Aware CompositionalNets A Wang, Y Sun, A Kortylewski, A Yuille Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020 | 48 | 2020 |
| PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning C Yang, A Kortylewski, C Xie, Y Cao, A Yuille Proceedings of the European Conference on Computer Vision (ECCV), 2020 | 47 | 2020 |
| Unsupervised footwear impression analysis and retrieval from crime scene data A Kortylewski, T Albrecht, T Vetter Asian Conference on Computer Vision, 644-658, 2014 | 43 | 2014 |
| Combining compositional models and deep networks for robust object classification under occlusion A Kortylewski, Q Liu, H Wang, Z Zhang, A Yuille Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 41 | 2020 |
| A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation J Mu, W Qiu, A Kortylewski, A Yuille, N Vasconcelos, X Wang Proceedings of the IEEE International Conference on Computer Vision, 2021 | 36 | 2021 |
| Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion A Kortylewski, Q Liu, A Wang, Y Sun, A Yuille International Journal of Computer Vision 129 (3), 736-760, 2020 | 36 | 2020 |
| Probabilistic Compositional Active Basis Models for Robust Pattern Recognition. A Kortylewski, T Vetter British Machine Vision Conference, 2016 | 29 | 2016 |
| SkelNetOn 2019 Dataset and Challenge on Deep Learning for Geometric Shape Understanding I Demir, C Hahn, K Leonard, G Morin, D Rahbani, A Panotopoulou, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 25 | 2019 |
| Multimodal image synthesis and editing: A survey F Zhan, Y Yu, R Wu, J Zhang, S Lu, L Liu, A Kortylewski, C Theobalt, ... arXiv preprint arXiv:2112.13592, 2022 | 23 | 2022 |
| Model-based image analysis for forensic shoe print recognition A Kortylewski University_of_Basel, 2017 | 23 | 2017 |
| NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation A Wang, A Kortylewski, A Yuille International Conference on Learning Representations (ICLR), 2021, 2021 | 19 | 2021 |
| Tdmpnet: Prototype network with recurrent top-down modulation for robust object classification under partial occlusion M Xiao, A Kortylewski, R Wu, S Qiao, W Shen, A Yuille European Conference on Computer Vision, 447-463, 2020 | 19 | 2020 |
| Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping P Kaushik, A Gain, A Kortylewski, A Yuille Advances in Neural Information Processing Systems, MetaLearn Workshop, 2021 | 17 | 2021 |
Alan YuilleProfessor of Cognitive Science and Computer Science, Johns Hopkins UniversityVerified email at jhu.edu
