| Lxmert: Learning cross-modality encoder representations from transformers H Tan, M Bansal Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 1878 | 2019 |
| Unifying vision-and-language tasks via text generation J Cho, J Lei, H Tan, M Bansal International Conference on Machine Learning, 1931-1942, 2021 | 322 | 2021 |
| A joint speaker-listener-reinforcer model for referring expressions L Yu, H Tan, M Bansal, TL Berg Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 266 | 2017 |
| Learning to navigate unseen environments: Back translation with environmental dropout H Tan, L Yu, M Bansal arXiv preprint arXiv:1904.04195, 2019 | 248 | 2019 |
| How much can clip benefit vision-and-language tasks? S Shen, LH Li, H Tan, M Bansal, A Rohrbach, KW Chang, Z Yao, ... arXiv preprint arXiv:2107.06383, 2021 | 243 | 2021 |
| Vokenization: Improving language understanding with contextualized, visual-grounded supervision H Tan, M Bansal arXiv preprint arXiv:2010.06775, 2020 | 98 | 2020 |
| Vimpac: Video pre-training via masked token prediction and contrastive learning H Tan, J Lei, T Wolf, M Bansal arXiv preprint arXiv:2106.11250, 2021 | 49 | 2021 |
| Enabling robots to understand incomplete natural language instructions using commonsense reasoning H Chen, H Tan, A Kuntz, M Bansal, R Alterovitz 2020 IEEE International Conference on Robotics and Automation (ICRA), 1963-1969, 2020 | 48 | 2020 |
| Diagnosing the environment bias in vision-and-language navigation Y Zhang, H Tan, M Bansal arXiv preprint arXiv:2005.03086, 2020 | 43 | 2020 |
| Envedit: Environment editing for vision-and-language navigation J Li, H Tan, M Bansal Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 36 | 2022 |
| The curse of performance instability in analysis datasets: Consequences, source, and suggestions X Zhou, Y Nie, H Tan, M Bansal arXiv preprint arXiv:2004.13606, 2020 | 33 | 2020 |
| Expressing visual relationships via language H Tan, F Dernoncourt, Z Lin, T Bui, M Bansal arXiv preprint arXiv:1906.07689, 2019 | 30 | 2019 |
| An Effective Framework for Weakly-Supervised Phrase Grounding Q Wang, H Tan, S Shen, M Mahoney, Z Yao Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 28* | 2020 |
| Improving cross-modal alignment in vision language navigation via syntactic information J Li, H Tan, M Bansal arXiv preprint arXiv:2104.09580, 2021 | 25 | 2021 |
| Modality-balanced models for visual dialogue H Kim, H Tan, M Bansal Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8091-8098, 2020 | 21 | 2020 |
| Vidlankd: Improving language understanding via video-distilled knowledge transfer Z Tang, J Cho, H Tan, M Bansal Advances in Neural Information Processing Systems 34, 24468-24481, 2021 | 19 | 2021 |
| Object ordering with bidirectional matchings for visual reasoning H Tan, M Bansal arXiv preprint arXiv:1804.06870, 2018 | 13 | 2018 |
| Source-target inference models for spatial instruction understanding H Tan, M Bansal Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 12 | 2018 |
| ArraMon: A joint navigation-assembly instruction interpretation task in dynamic environments H Kim, A Zala, G Burri, H Tan, M Bansal arXiv preprint arXiv:2011.07660, 2020 | 11 | 2020 |
| Tiny-attention adapter: Contexts are more important than the number of parameters H Zhao, H Tan, H Mei arXiv preprint arXiv:2211.01979, 2022 | 5 | 2022 |
Mohit BansalProfessor, Computer Science, UNC Chapel HillDirección de correo verificada de cs.unc.edu
