| Learning video object segmentation with visual memory P Tokmakov, K Alahari, C Schmid Proceedings of the IEEE International Conference on Computer Vision, 4481-4490, 2017 | 304 | 2017 |
| Learning motion patterns in videos P Tokmakov, K Alahari, C Schmid Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 265 | 2017 |
| Learning compositional representations for few-shot recognition P Tokmakov, YX Wang, M Hebert Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 92 | 2019 |
| Learning to segment moving objects P Tokmakov, C Schmid, K Alahari International Journal of Computer Vision 127, 282-301, 2019 | 77 | 2019 |
| Tao: A large-scale benchmark for tracking any object A Dave, T Khurana, P Tokmakov, C Schmid, D Ramanan Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 74 | 2020 |
| A structured model for action detection Y Zhang, P Tokmakov, M Hebert, C Schmid Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 74 | 2019 |
| Weakly-supervised semantic segmentation using motion cues P Tokmakov, K Alahari, C Schmid Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 72* | 2016 |
| One click mining: Interactive local pattern discovery through implicit preference and performance learning M Boley, M Mampaey, B Kang, P Tokmakov, S Wrobel Proceedings of the ACM SIGKDD workshop on interactive data exploration and …, 2013 | 71 | 2013 |
| Learning to track with object permanence P Tokmakov, J Li, W Burgard, A Gaidon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 69 | 2021 |
| Towards segmenting anything that moves A Dave, P Tokmakov, D Ramanan Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 57* | 2019 |
| Relational linear programming K Kersting, M Mladenov, P Tokmakov Artificial Intelligence 244, 188-216, 2017 | 26* | 2017 |
| Unsupervised learning of video representations via dense trajectory clustering P Tokmakov, M Hebert, C Schmid Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 18 | 2020 |
| Heterogeneous-agent trajectory forecasting incorporating class uncertainty B Ivanovic, KH Lee, P Tokmakov, B Wulfe, R Mcllister, A Gaidon, ... 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 13 | 2022 |
| A study on action detection in the wild Y Zhang, P Tokmakov, M Hebert, C Schmid arXiv preprint arXiv:1904.12993, 2019 | 11 | 2019 |
| Discovering objects that can move Z Bao, P Tokmakov, A Jabri, YX Wang, A Gaidon, M Hebert Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 8 | 2022 |
| Learning to track any object A Dave, P Tokmakov, C Schmid, D Ramanan arXiv preprint arXiv:1910.11844, 2019 | 8 | 2019 |
| Towards latent attribute discovery from triplet similarities I Nigam, P Tokmakov, D Ramanan Proceedings of the IEEE/CVF International Conference on Computer Vision, 402-410, 2019 | 7 | 2019 |
| Object Permanence Emerges in a Random Walk along Memory P Tokmakov, A Jabri, J Li, A Gaidon arXiv preprint arXiv:2204.01784, 2022 | 1 | 2022 |
| Breaking the" Object" in Video Object Segmentation P Tokmakov, J Li, A Gaidon arXiv preprint arXiv:2212.06200, 2022 | | 2022 |
| System and method for tracking occluded objects PV Tokmakov, RA Ambrus, W Burgard, AD Gaidon US Patent App. 17/203,583, 2022 | | 2022 |
