| Sentry: Selective entropy optimization via committee consistency for unsupervised domain adaptation V Prabhu, S Khare, D Kartik, J Hoffman Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 159 | 2021 |
| Active domain adaptation via clustering uncertainty-weighted embeddings V Prabhu, A Chandrasekaran, K Saenko, J Hoffman Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 148 | 2021 |
| Do explanations make VQA models more predictable to a human? A Chandrasekaran, V Prabhu, D Yadav, P Chattopadhyay, D Parikh EMNLP 2018, 2018 | 112 | 2018 |
| Evaluating visual conversational agents via cooperative human-ai games P Chattopadhyay, D Yadav, V Prabhu, A Chandrasekaran, A Das, S Lee, ... Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 5 …, 2017 | 92 | 2017 |
| It takes two to tango: Towards theory of AI's mind A Chandrasekaran, D Yadav, P Chattopadhyay, V Prabhu, D Parikh Chalearn Looking at People Workshop, CVPR, 2017, 2017 | 81 | 2017 |
| Few-shot learning for dermatological disease diagnosis V Prabhu, A Kannan, M Ravuri, M Chaplain, D Sontag, X Amatriain Machine Learning for Healthcare Conference, 532-552, 2019 | 68 | 2019 |
| Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks M Goldblum, H Souri, R Ni, M Shu, V Prabhu, G Somepalli, ... Advances in Neural Information Processing Systems 36, 2024 | 48 | 2024 |
| The promise of premise: Harnessing question premises in visual question answering A Mahendru, V Prabhu, A Mohapatra, D Batra, S Lee EMNLP 2017, 2017 | 47 | 2017 |
| UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models. A Krishnakumar, V Prabhu, S Sudhakar, J Hoffman BMVC, 143, 2021 | 29 | 2021 |
| Lance: Stress-testing visual models by generating language-guided counterfactual images V Prabhu, S Yenamandra, P Chattopadhyay, J Hoffman Advances in Neural Information Processing Systems 36, 25165-25184, 2023 | 24 | 2023 |
| Augco: augmentation consistency-guided self-training for source-free domain adaptive semantic segmentation V Prabhu, S Khare, D Kartik, J Hoffman arXiv preprint arXiv:2107.10140, 2021 | 22* | 2021 |
| xgen-mm (blip-3): A family of open large multimodal models L Xue, M Shu, A Awadalla, J Wang, A Yan, S Purushwalkam, H Zhou, ... arXiv preprint arXiv:2408.08872, 2024 | 19 | 2024 |
| S4t: Source-free domain adaptation for semantic segmentation via self-supervised selective self-training V Prabhu, S Khare, D Kartik, J Hoffman arXiv preprint arXiv:2107.10140 2 (3), 6, 2021 | 17 | 2021 |
| Prototypical clustering networks for dermatological disease diagnosis V Prabhu, A Kannan, M Ravuri, M Chablani, D Sontag, X Amatriain arXiv preprint arXiv:1811.03066, 2018 | 17 | 2018 |
| Facts: First amplify correlations and then slice to discover bias S Yenamandra, P Ramesh, V Prabhu, J Hoffman Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 14 | 2023 |
| Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency V Prabhu, S Yenamandra, A Singh, J Hoffman NeurIPS 2022, 2022 | 14 | 2022 |
| Open set medical diagnosis V Prabhu, A Kannan, GJ Tso, N Katariya, M Chablani, D Sontag, ... ML4H workshop, NeurIPS 2019, 2019 | 10 | 2019 |
| Can domain adaptation make object recognition work for everyone? V Prabhu, RR Selvaraju, J Hoffman, N Naik Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 8 | 2022 |
| Systems and methods for responding to healthcare inquiries A Kannan, M Ravuri, V Rodrigues, V Venkataraman, TSO Geoffrey, ... US Patent 10,847,265, 2020 | 8 | 2020 |
| Bridging the sim2real gap with care: Supervised detection adaptation with conditional alignment and reweighting V Prabhu, D Acuna, A Liao, R Mahmood, MT Law, J Hoffman, S Fidler, ... arXiv preprint arXiv:2302.04832, 2023 | 7 | 2023 |
Judy HoffmanAssistant Professor, Georgia TechEmail confirmado em gatech.edu
