| Vivit: A video vision transformer A Arnab, M Dehghani, G Heigold, C Sun, M Lučić, C Schmid Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 1365 | 2021 |
| Attention bottlenecks for multimodal fusion A Nagrani, S Yang, A Arnab, A Jansen, C Schmid, C Sun Advances in Neural Information Processing Systems 34, 14200-14213, 2021 | 352 | 2021 |
| Higher order conditional random fields in deep neural networks A Arnab, S Jayasumana, S Zheng, PHS Torr Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 303* | 2016 |
| On the robustness of semantic segmentation models to adversarial attacks A Arnab, O Miksik, PHS Torr IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 888-897, 2018 | 293 | 2018 |
| Pixelwise instance segmentation with a dynamically instantiated network A Arnab, PHS Torr Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 271 | 2017 |
| Exploiting temporal context for 3D human pose estimation in the wild A Arnab, C Doersch, A Zisserman Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 231 | 2019 |
| Dual graph convolutional network for semantic segmentation L Zhang, X Li, A Arnab, K Yang, Y Tong, PHS Torr arXiv preprint arXiv:1909.06121, 2019 | 187 | 2019 |
| Weakly-and Semi-Supervised Panoptic Segmentation Q Li, A Arnab, PHS Torr Proceedings of the European Conference on Computer Vision (ECCV), 102-118, 2018 | 185 | 2018 |
| Simple open-vocabulary object detection M Minderer, A Gritsenko, A Stone, M Neumann, D Weissenborn, ... European Conference on Computer Vision, 728-755, 2022 | 159 | 2022 |
| Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction A Arnab, S Zheng, S Jayasumana, B Romera-Paredes, M Larsson, ... IEEE Signal Processing Magazine 35 (1), 37-52, 2018 | 156 | 2018 |
| Multiview transformers for video recognition S Yan, X Xiong, A Arnab, Z Lu, M Zhang, C Sun, C Schmid Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 150 | 2022 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 127 | 2023 |
| Dynamic graph message passing networks L Zhang, D Xu, A Arnab, PHS Torr Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 122 | 2020 |
| Tokenlearner: Adaptive space-time tokenization for videos M Ryoo, AJ Piergiovanni, A Arnab, M Dehghani, A Angelova Advances in Neural Information Processing Systems 34, 12786-12797, 2021 | 101 | 2021 |
| End-to-end generative pretraining for multimodal video captioning PH Seo, A Nagrani, A Arnab, C Schmid Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 99 | 2022 |
| Tokenlearner: What can 8 learned tokens do for images and videos? MS Ryoo, AJ Piergiovanni, A Arnab, M Dehghani, A Angelova arXiv preprint arXiv:2106.11297, 2021 | 90 | 2021 |
| The efficiency misnomer M Dehghani, A Arnab, L Beyer, A Vaswani, Y Tay arXiv preprint arXiv:2110.12894, 2021 | 74 | 2021 |
| Bottom-up instance segmentation using deep higher-order crfs A Arnab, PHS Torr Proceedings of the British Machine Vision Conference (BMVC), 2016 | 69 | 2016 |
| Holistic, Instance-Level Human Parsing Q Li, A Arnab, PHS Torr Proceedings of the British Machine Vision Conference (BMVC), 2017 | 65 | 2017 |
| Scenic: A JAX library for computer vision research and beyond M Dehghani, A Gritsenko, A Arnab, M Minderer, Y Tay Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 55 | 2022 |
