| Unsupervised monocular depth estimation with left-right consistency C Godard, O Mac Aodha, GJ Brostow Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 3788 | 2017 |
| Digging into self-supervised monocular depth estimation C Godard, O Mac Aodha, M Firman, GJ Brostow Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 2845 | 2019 |
| The inaturalist species classification and detection dataset G Van Horn, O Mac Aodha, Y Song, Y Cui, C Sun, A Shepard, H Adam, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 2042* | 2018 |
| Fine-grained image analysis with deep learning: A survey XS Wei, YZ Song, O Mac Aodha, J Wu, Y Peng, J Tang, J Yang, ... IEEE transactions on pattern analysis and machine intelligence 44 (12), 8927 …, 2021 | 348 | 2021 |
| The temporal opportunist: Self-supervised multi-frame monocular depth J Watson, O Mac Aodha, V Prisacariu, G Brostow, M Firman Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 333 | 2021 |
| Bat detective—Deep learning tools for bat acoustic signal detection O Mac Aodha, R Gibb, KE Barlow, E Browning, M Firman, R Freeman, ... PLoS computational biology 14 (3), e1005995, 2018 | 260 | 2018 |
| Patch based synthesis for single depth image super-resolution O Mac Aodha, NDF Campbell, A Nair, GJ Brostow Computer Vision–ECCV 2012: 12th European Conference on Computer Vision …, 2012 | 255 | 2012 |
| Hierarchical subquery evaluation for active learning on a graph O Mac Aodha, NDF Campbell, J Kautz, GJ Brostow Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 251 | 2014 |
| Structured prediction of unobserved voxels from a single depth image M Firman, O Mac Aodha, S Julier, GJ Brostow Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 210 | 2016 |
| Presence-only geographical priors for fine-grained image classification O Mac Aodha, E Cole, P Perona Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 183 | 2019 |
| Benchmarking representation learning for natural world image collections G Van Horn, E Cole, S Beery, K Wilber, S Belongie, O Mac Aodha Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 175 | 2021 |
| When does contrastive visual representation learning work? E Cole, X Yang, K Wilber, O Mac Aodha, S Belongie Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 155* | 2022 |
| Multi-label learning from single positive labels E Cole, O Mac Aodha, T Lorieul, P Perona, D Morris, N Jojic Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 135* | 2021 |
| Learning a confidence measure for optical flow O Mac Aodha, A Humayun, M Pollefeys, GJ Brostow IEEE transactions on pattern analysis and machine intelligence 35 (5), 1107-1120, 2012 | 130 | 2012 |
| Becoming the expert-interactive multi-class machine teaching E Johns, O Mac Aodha, GJ Brostow Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 94 | 2015 |
| Teaching categories to human learners with visual explanations O Mac Aodha, S Su, Y Chen, P Perona, Y Yue Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 85 | 2018 |
| Learning to find occlusion regions A Humayun, O Mac Aodha, GJ Brostow CVPR 2011, 2161-2168, 2011 | 78 | 2011 |
| Learning stereo from single images J Watson, OM Aodha, D Turmukhambetov, GJ Brostow, M Firman Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 76 | 2020 |
| My Text in Your Handwriting TSF Haines, O Mac Aodha, GJ Brostow ACM Transactions on Graphics (TOG) 35 (3), 26, 2016 | 74* | 2016 |
| Predicting depth from image data using a statistical model C Godard, O MacAodha, G Brostow US Patent 11,100,401, 2021 | 73 | 2021 |
Gabriel J. BrostowProfessor of Computer Science, University College LondonVerified email at cs.ucl.ac.uk
