| 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 | 280 | 2021 |
| Higher order conditional random fields in deep neural networks A Arnab, S Jayasumana, S Zheng, PHS Torr European conference on computer vision, 524-540, 2016 | 272* | 2016 |
| Pixelwise instance segmentation with a dynamically instantiated network A Arnab, PHS Torr Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 226 | 2017 |
| 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 | 210 | 2018 |
| 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 | 159 | 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 | 144 | 2018 |
| 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 | 119 | 2018 |
| 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 | 114 | 2019 |
| 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 | 75 | 2020 |
| Bottom-up instance segmentation using deep higher-order crfs A Arnab, PHS Torr Proceedings of the British Machine Vision Conference (BMVC), 2016 | 60 | 2016 |
| Holistic, Instance-Level Human Parsing Q Li, A Arnab, PHS Torr Proceedings of the British Machine Vision Conference (BMVC), 2017 | 48 | 2017 |
| 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 | 46 | 2021 |
| A projected gradient descent method for CRF inference allowing end-to-end training of arbitrary pairwise potentials M Larsson, A Arnab, F Kahl, S Zheng, P Torr International Conference on Energy Minimization Methods in Computer Vision …, 2017 | 30* | 2017 |
| 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 | 19 | 2021 |
| The efficiency misnomer M Dehghani, A Arnab, L Beyer, A Vaswani, Y Tay arXiv preprint arXiv:2110.12894, 2021 | 17 | 2021 |
| Semanticpaint: A framework for the interactive segmentation of 3d scenes S Golodetz, M Sapienza, JPC Valentin, V Vineet, MM Cheng, A Arnab, ... arXiv preprint arXiv:1510.03727, 2015 | 17 | 2015 |
| Meta-Learning Deep Visual Words for Fast Video Object Segmentation HS Behl, M Naja, A Arnab, PHS Torr 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 15 | 2020 |
| 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 | 14 | 2022 |
| Semanticpaint: interactive segmentation and learning of 3d worlds S Golodetz, M Sapienza, JPC Valentin, V Vineet, MM Cheng, ... ACM SIGGRAPH 2015 Emerging Technologies, 1-1, 2015 | 14 | 2015 |
| 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 | 12 | 2022 |
