| Grad-cam: Visual explanations from deep networks via gradient-based localization RR Selvaraju, M Cogswell, A Das, R Vedantam, D Parikh, D Batra Proceedings of the IEEE international conference on computer vision, 618-626, 2017 | 26226 | 2017 |
| Diverse beam search: Decoding diverse solutions from neural sequence models AK Vijayakumar, M Cogswell, RR Selvaraju, Q Sun, S Lee, D Crandall, ... arXiv preprint arXiv:1610.02424, 2016 | 586 | 2016 |
| Reducing overfitting in deep networks by decorrelating representations M Cogswell, F Ahmed, R Girshick, L Zitnick, D Batra arXiv preprint arXiv:1511.06068, 2015 | 509 | 2015 |
| Why m heads are better than one: Training a diverse ensemble of deep networks S Lee, S Purushwalkam, M Cogswell, D Crandall, D Batra arXiv preprint arXiv:1511.06314, 2015 | 347 | 2015 |
| Proceedings of the IEEE international conference on computer vision RR Selvaraju, M Cogswell, A Das, R Vedantam, D Parikh, D Batra Proceedings of the IEEE international conference on computer vision [J], 2017 | 278 | 2017 |
| Diverse beam search for improved description of complex scenes A Vijayakumar, M Cogswell, R Selvaraju, Q Sun, S Lee, D Crandall, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 258 | 2018 |
| Stochastic multiple choice learning for training diverse deep ensembles S Lee, S Purushwalkam Shiva Prakash, M Cogswell, V Ranjan, ... Advances in Neural Information Processing Systems 29, 2016 | 219 | 2016 |
| Grad-CAM: Visual explanations from deep networks via gradient-based localization. arXiv 2016 RR Selvaraju, M Cogswell, A Das, R Vedantam, D Parikh, D Batra arXiv preprint arXiv:1610.02391 6, 2022 | 101 | 2022 |
| Emergence of compositional language with deep generational transmission M Cogswell, J Lu, S Lee, D Parikh, D Batra arXiv preprint arXiv:1904.09067, 2019 | 61 | 2019 |
| Grad-CAM: Why did you say that? arXiv 2016 RR Selvaraju, A Das, R Vedantam, M Cogswell, D Parikh, D Batra arXiv preprint arXiv:1611.07450, 0 | 60 | |
| Running students' software tests against each others' code: new life for an old" gimmick" SH Edwards, Z Shams, M Cogswell, RC Senkbeil Proceedings of the 43rd ACM technical symposium on Computer Science …, 2012 | 49 | 2012 |
| Trigger hunting with a topological prior for trojan detection X Hu, X Lin, M Cogswell, Y Yao, S Jha, C Chen arXiv preprint arXiv:2110.08335, 2021 | 46 | 2021 |
| Dress: Instructing large vision-language models to align and interact with humans via natural language feedback Y Chen, K Sikka, M Cogswell, H Ji, A Divakaran Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 45 | 2024 |
| Measuring and improving chain-of-thought reasoning in vision-language models Y Chen, K Sikka, M Cogswell, H Ji, A Divakaran arXiv preprint arXiv:2309.04461, 2023 | 22 | 2023 |
| Combining the best of graphical models and convnets for semantic segmentation M Cogswell, X Lin, S Purushwalkam, D Batra arXiv preprint arXiv:1412.4313, 2014 | 22 | 2014 |
| Dialog without dialog data: Learning visual dialog agents from VQA data M Cogswell, J Lu, R Jain, S Lee, D Parikh, D Batra Advances in Neural Information Processing Systems 33, 19988-19999, 2020 | 15 | 2020 |
| Unpacking large language models with conceptual consistency P Sahu, M Cogswell, Y Gong, A Divakaran arXiv preprint arXiv:2209.15093, 2022 | 13 | 2022 |
| Probing conceptual understanding of large visual-language models M Schiappa, R Abdullah, S Azad, J Claypoole, M Cogswell, A Divakaran, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 11 | 2024 |
| Improving users' mental model with attention‐directed counterfactual edits K Alipour, A Ray, X Lin, M Cogswell, JP Schulze, Y Yao, GT Burachas Applied AI Letters 2 (4), e47, 2021 | 10 | 2021 |
| Comprehension based question answering using Bloom's Taxonomy P Sahu, M Cogswell, S Rutherford-Quach, A Divakaran arXiv preprint arXiv:2106.04653, 2021 | 6 | 2021 |
Dhruv BatraGeorgia Tech | Prev: FAIR (Meta AI)Bestätigte E-Mail-Adresse bei gatech.edu
