| Evaluation of deep convolutional nets for document image classification and retrieval AW Harley, A Ufkes, KG Derpanis 2015 13th International Conference on Document Analysis and Recognition …, 2015 | 498 | 2015 |
| Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness JJ Yu, AW Harley, KG Derpanis Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 494 | 2016 |
| An interactive node-link visualization of convolutional neural networks AW Harley Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las …, 2015 | 194 | 2015 |
| Segmentation-aware convolutional networks using local attention masks AW Harley, KG Derpanis, I Kokkinos Proceedings of the IEEE International Conference on Computer Vision, 5038-5047, 2017 | 184 | 2017 |
| Adversarial inverse graphics networks: Learning 2d-to-3d lifting and image-to-image translation from unpaired supervision HYF Tung, AW Harley, W Seto, K Fragkiadaki 2017 IEEE International Conference on Computer Vision (ICCV), 4364-4372, 2017 | 171 | 2017 |
| VR facial animation via multiview image translation SE Wei, J Saragih, T Simon, AW Harley, S Lombardi, M Perdoch, A Hypes, ... ACM Transactions on Graphics (ToG) 38 (4), 1-16, 2019 | 151 | 2019 |
| Particle video revisited: Tracking through occlusions using point trajectories AW Harley, Z Fang, K Fragkiadaki European Conference on Computer Vision, 59-75, 2022 | 149 | 2022 |
| Simple-bev: What really matters for multi-sensor bev perception? AW Harley, Z Fang, J Li, R Ambrus, K Fragkiadaki 2023 IEEE International Conference on Robotics and Automation (ICRA), 2759-2765, 2023 | 118* | 2023 |
| Pointodyssey: A large-scale synthetic dataset for long-term point tracking Y Zheng, AW Harley, B Shen, G Wetzstein, LJ Guibas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 110 | 2023 |
| Reward learning from narrated demonstrations HY Tung, AW Harley, LK Huang, K Fragkiadaki Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 45 | 2018 |
| Tidee: Tidying up novel rooms using visuo-semantic commonsense priors G Sarch, Z Fang, AW Harley, P Schydlo, MJ Tarr, S Gupta, K Fragkiadaki European conference on computer vision, 480-496, 2022 | 41 | 2022 |
| Learning from unlabelled videos using contrastive predictive neural 3d mapping AW Harley, SK Lakshmikanth, F Li, X Zhou, HYF Tung, K Fragkiadaki arXiv preprint arXiv:1906.03764, 2019 | 33* | 2019 |
| CoCoNets: Continuous contrastive 3D scene representations S Lal, M Prabhudesai, I Mediratta, AW Harley, K Fragkiadaki Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 28 | 2021 |
| Move to see better: Self-improving embodied object detection Z Fang, A Jain, G Sarch, AW Harley, K Fragkiadaki arXiv preprint arXiv:2012.00057, 2020 | 25 | 2020 |
| Learning dense convolutional embeddings for semantic segmentation AW Harley, KG Derpanis, I Kokkinos arXiv preprint arXiv:1511.04377, 2015 | 25 | 2015 |
| Embodied language grounding with 3d visual feature representations M Prabhudesai, HYF Tung, SA Javed, M Sieb, AW Harley, K Fragkiadaki Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 23 | 2020 |
| Disentangling 3d prototypical networks for few-shot concept learning M Prabhudesai, S Lal, D Patil, HY Tung, AW Harley, K Fragkiadaki arXiv preprint arXiv:2011.03367, 2020 | 20 | 2020 |
| Adversarial inverse graphics networks: Learning 2D-to-3D lifting and image-to-image translation from unpaired supervision HY Fish Tung, AW Harley, W Seto, K Fragkiadaki Proceedings of the IEEE International Conference on Computer Vision, 4354-4362, 2017 | 20 | 2017 |
| MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds J Lei, Y Weng, A Harley, L Guibas, K Daniilidis arXiv preprint arXiv:2405.17421, 2024 | 18 | 2024 |
| Embodied language grounding with implicit 3d visual feature representations M Prabhudesai, HYF Tung, SA Javed, M Sieb, AW Harley, K Fragkiadaki | 10 | 2019 |
Katerina FragkiadakiAssociate Professor, Carnegie Mellon UniversityΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα cs.cmu.edu
