| Visualbert: A simple and performant baseline for vision and language LH Li, M Yatskar, D Yin, CJ Hsieh, KW Chang arXiv preprint arXiv:1908.03557, 2019 | 1962 | 2019 |
| Men also like shopping: Reducing gender bias amplification using corpus-level constraints J Zhao, T Wang, M Yatskar, V Ordonez, KW Chang arXiv preprint arXiv:1707.09457, 2017 | 1193 | 2017 |
| Neural motifs: Scene graph parsing with global context R Zellers, M Yatskar, S Thomson, Y Choi Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 1118 | 2018 |
| Gender bias in coreference resolution: Evaluation and debiasing methods J Zhao, T Wang, M Yatskar, V Ordonez, KW Chang arXiv preprint arXiv:1804.06876, 2018 | 1023 | 2018 |
| QuAC: Question answering in context E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer arXiv preprint arXiv:1808.07036, 2018 | 945 | 2018 |
| Don't take the easy way out: Ensemble based methods for avoiding known dataset biases C Clark, M Yatskar, L Zettlemoyer arXiv preprint arXiv:1909.03683, 2019 | 512 | 2019 |
| Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations T Wang, J Zhao, M Yatskar, KW Chang, V Ordonez Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 511 | 2019 |
| Gender bias in contextualized word embeddings J Zhao, T Wang, M Yatskar, R Cotterell, V Ordonez, KW Chang arXiv preprint arXiv:1904.03310, 2019 | 463 | 2019 |
| Neural amr: Sequence-to-sequence models for parsing and generation I Konstas, S Iyer, M Yatskar, Y Choi, L Zettlemoyer arXiv preprint arXiv:1704.08381, 2017 | 364 | 2017 |
| Situation Recognition: Visual Semantic Role Labeling for Image Understanding M Yatskar, L Zettlemoyer, A Farhadi Conference on Computer Vision and Pattern Recognition, 2016 | 302 | 2016 |
| Robothor: An open simulation-to-real embodied ai platform M Deitke, W Han, A Herrasti, A Kembhavi, E Kolve, R Mottaghi, J Salvador, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 259 | 2020 |
| For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia M Yatskar, B Pang, C Danescu-Niculescu-Mizil, L Lee arXiv preprint arXiv:1008.1986, 2010 | 218 | 2010 |
| Language in a bottle: Language model guided concept bottlenecks for interpretable image classification Y Yang, A Panagopoulou, S Zhou, D Jin, C Callison-Burch, M Yatskar Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 175 | 2023 |
| What does BERT with vision look at? LH Li, M Yatskar, D Yin, CJ Hsieh, KW Chang Proceedings of the 58th annual meeting of the association for computational …, 2020 | 155 | 2020 |
| A qualitative comparison of CoQA, SQuAD 2.0 and QuAC M Yatskar arXiv preprint arXiv:1809.10735, 2018 | 114 | 2018 |
| Grounded situation recognition S Pratt, M Yatskar, L Weihs, A Farhadi, A Kembhavi Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 102 | 2020 |
| Visual semantic role labeling for video understanding A Sadhu, T Gupta, M Yatskar, R Nevatia, A Kembhavi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 71 | 2021 |
| Stating the obvious: Extracting visual common sense knowledge M Yatskar, V Ordonez, A Farhadi Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 70 | 2016 |
| Visualbert: A simple and performant baseline for vision and language. arXiv 2019 LH Li, M Yatskar, D Yin, CJ Hsieh, KW Chang arXiv preprint arXiv:1908.03557 2, 0 | 64 | |
| See No Evil, Say No Evil: Description Generation from Densely Labeled Images M Yatskar, M Galley, L Vanderwende, L Zettlemoyer Lexical and Computational Semantics (* SEM 2014), 110, 2014 | 63 | 2014 |