| Lxmert: Learning cross-modality encoder representations from transformers H Tan, M Bansal Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 2869 | 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 | 565 | 2021 |
| 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 | 461 | 2021 |
| Learning to navigate unseen environments: Back translation with environmental dropout H Tan, L Yu, M Bansal arXiv preprint arXiv:1904.04195, 2019 | 357 | 2019 |
| Lrm: Large reconstruction model for single image to 3d Y Hong, K Zhang, J Gu, S Bi, Y Zhou, D Liu, F Liu, K Sunkavalli, T Bui, ... arXiv preprint arXiv:2311.04400, 2023 | 325 | 2023 |
| 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 | 310 | 2017 |
| Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model J Li, H Tan, K Zhang, Z Xu, F Luan, Y Xu, Y Hong, K Sunkavalli, ... arXiv preprint arXiv:2311.06214, 2023 | 208 | 2023 |
| Vokenization: Improving language understanding with contextualized, visual-grounded supervision H Tan, M Bansal arXiv preprint arXiv:2010.06775, 2020 | 136 | 2020 |
| Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model Y Xu, H Tan, F Luan, S Bi, P Wang, J Li, Z Shi, K Sunkavalli, G Wetzstein, ... arXiv preprint arXiv:2311.09217, 2023 | 133 | 2023 |
| Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction P Wang, H Tan, S Bi, Y Xu, F Luan, K Sunkavalli, W Wang, Z Xu, K Zhang arXiv preprint arXiv:2311.12024, 2023 | 88 | 2023 |
| 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 | 87 | 2021 |
| 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 | 86 | 2022 |
| Gs-lrm: Large reconstruction model for 3d gaussian splatting K Zhang, S Bi, H Tan, Y Xiangli, N Zhao, K Sunkavalli, Z Xu European Conference on Computer Vision, 1-19, 2024 | 79 | 2024 |
| Scaling data generation in vision-and-language navigation Z Wang, J Li, Y Hong, Y Wang, Q Wu, M Bansal, S Gould, H Tan, Y Qiao Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 65 | 2023 |
| Expressing visual relationships via language H Tan, F Dernoncourt, Z Lin, T Bui, M Bansal arXiv preprint arXiv:1906.07689, 2019 | 65 | 2019 |
| Diagnosing the environment bias in vision-and-language navigation Y Zhang, H Tan, M Bansal arXiv preprint arXiv:2005.03086, 2020 | 63 | 2020 |
| 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 | 62 | 2020 |
| MeshLRM: Large Reconstruction Model for High-Quality Meshes X Wei, K Zhang, S Bi, H Tan, F Luan, V Deschaintre, K Sunkavalli, H Su, ... arXiv preprint arXiv:2404.12385, 2024 | 48 | 2024 |
| 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 | 47 | 2020 |
| 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 | 44* | 2020 |
Mohit BansalParker Distinguished Professor, Computer Science, UNC Chapel HillPreverjeni e-poštni naslov na cs.unc.edu
