| Masked-attention mask transformer for universal image segmentation B Cheng, I Misra, AG Schwing, A Kirillov, R Girdhar Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 1857 | 2022 |
| Semantic image inpainting with deep generative models RA Yeh, C Chen, T Yian Lim, AG Schwing, M Hasegawa-Johnson, MN Do Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1404 | 2017 |
| Per-pixel classification is not all you need for semantic segmentation B Cheng, A Schwing, A Kirillov Advances in neural information processing systems 34, 17864-17875, 2021 | 1314 | 2021 |
| Efficient deep learning for stereo matching W Luo, AG Schwing, R Urtasun Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 925 | 2016 |
| Real-time 3D imaging of Haines jumps in porous media flow S Berg, H Ott, SA Klapp, A Schwing, R Neiteler, N Brussee, A Makurat, ... Proceedings of the National Academy of Sciences 110 (10), 3755-3759, 2013 | 665 | 2013 |
| Convolutional image captioning J Aneja, A Deshpande, AG Schwing Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 455 | 2018 |
| Fully connected deep structured networks AG Schwing, R Urtasun arXiv preprint arXiv:1503.02351, 2015 | 385 | 2015 |
| Videomatch: Matching based video object segmentation YT Hu, JB Huang, AG Schwing Proceedings of the European conference on computer vision (ECCV), 54-70, 2018 | 327 | 2018 |
| Learning deep structured models LC Chen, A Schwing, A Yuille, R Urtasun International Conference on Machine Learning, 1785-1794, 2015 | 308 | 2015 |
| Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model HK Cheng, AG Schwing European Conference on Computer Vision, 640-658, 2022 | 304 | 2022 |
| Out of the box: Reasoning with graph convolution nets for factual visual question answering M Narasimhan, S Lazebnik, A Schwing Advances in neural information processing systems 31, 2018 | 269 | 2018 |
| From connected pathway flow to ganglion dynamics M Rücker, S Berg, RT Armstrong, A Georgiadis, H Ott, A Schwing, ... Geophysical Research Letters 42 (10), 3888-3894, 2015 | 264 | 2015 |
| Generative modeling using the sliced wasserstein distance I Deshpande, Z Zhang, AG Schwing Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 253 | 2018 |
| Instance-aware, context-focused, and memory-efficient weakly supervised object detection Z Ren, Z Yu, X Yang, MY Liu, YJ Lee, AG Schwing, J Kautz Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 231 | 2020 |
| Learning to segment under various forms of weak supervision J Xu, AG Schwing, R Urtasun Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 226 | 2015 |
| Maskrnn: Instance level video object segmentation YT Hu, JB Huang, A Schwing Advances in neural information processing systems 30, 2017 | 219 | 2017 |
| Max-sliced wasserstein distance and its use for gans I Deshpande, YT Hu, R Sun, A Pyrros, N Siddiqui, S Koyejo, Z Zhao, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 216 | 2019 |
| Diverse and accurate image description using a variational auto-encoder with an additive gaussian encoding space L Wang, A Schwing, S Lazebnik Advances in Neural Information Processing Systems 30, 2017 | 206 | 2017 |
| Agriculture-vision: A large aerial image database for agricultural pattern analysis MT Chiu, X Xu, Y Wei, Z Huang, AG Schwing, R Brunner, H Khachatrian, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 203 | 2020 |
| Dynamic Bayesian networks for student modeling T Käser, S Klingler, AG Schwing, M Gross IEEE Transactions on Learning Technologies 10 (4), 450-462, 2017 | 186 | 2017 |
