| Grad-CAM: Why did you say that? RR Selvaraju, A Das, R Vedantam, M Cogswell, D Parikh, D Batra IEEE International Conference on Computer Vision (ICCV), 2017, 2016 | 14994* | 2016 |
| CIDEr: Consensus-based Image Description Evaluation R Vedantam, CL Zitnick, D Parikh IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, 2014 | 3505 | 2014 |
| Microsoft coco captions: Data collection and evaluation server X Chen, H Fang, TY Lin, R Vedantam, S Gupta, P Dollár, CL Zitnick arXiv preprint arXiv:1504.00325, 2015 | 1816 | 2015 |
| Context-aware captions from context-agnostic supervision R Vedantam, S Bengio, K Murphy, D Parikh, G Chechik IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 2017 | 148 | 2017 |
| Counting Everyday Objects in Everyday Scenes P Chattopadhyay, R Vedantam, RS Ramprasaath, D Batra, D Parikh IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 2016 | 140 | 2016 |
| Generative Models of Visually Grounded Imagination R Vedantam, I Fischer, J Huang, K Murphy International Conference on Learning Representations (ICLR), 2018, 2018 | 129 | 2018 |
| Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes S Kottur, R Vedantam, JMF Moura, D Parikh IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, 2015 | 116 | 2015 |
| Adopting abstract images for semantic scene understanding CL Zitnick, R Vedantam, D Parikh IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014 | 100 | 2014 |
| Learning Common Sense Through Visual Abstraction R Vedantam, X Lin, T Batra, CL Zitnick, D Parikh IEEE International Conference on Computer Vision (ICCV), 2015, 2015 | 94 | 2015 |
| Probabilistic neural symbolic models for interpretable visual question answering R Vedantam, K Desai, S Lee, M Rohrbach, D Batra, D Parikh International Conference on Machine Learning, 6428-6437, 2019 | 72 | 2019 |
| Sound-word2vec: Learning word representations grounded in sounds AK Vijayakumar, R Vedantam, D Parikh Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017, 2017 | 29 | 2017 |
| Learning optimal representations with the decodable information bottleneck Y Dubois, D Kiela, DJ Schwab, R Vedantam Advances in Neural Information Processing Systems 33, 18674-18690, 2020 | 27 | 2020 |
| Curi: A benchmark for productive concept learning under uncertainty R Vedantam, A Szlam, M Nickel, A Morcos, BM Lake International Conference on Machine Learning, 10519-10529, 2021 | 19 | 2021 |
| An empirical investigation of domain generalization with empirical risk minimizers R Vedantam, D Lopez-Paz, DJ Schwab Advances in Neural Information Processing Systems 34, 28131-28143, 2021 | 16 | 2021 |
| Coat: Measuring object compositionality in emergent representations S Xie, AS Morcos, SC Zhu, R Vedantam International Conference on Machine Learning, 24388-24413, 2022 | 4 | 2022 |
| DS-VIC: Unsupervised Discovery of Decision States for Transfer in RL N Modhe, P Chattopadhyay, M Sharma, A Das, D Parikh, D Batra, ... | 3* | |
| Hyperbolic Image-Text Representations K Desai, M Nickel, T Rajpurohit, J Johnson, R Vedantam arXiv preprint arXiv:2304.09172, 2023 | | 2023 |
| Improving Selective Visual Question Answering by Learning From Your Peers C Dancette, S Whitehead, R Maheshwary, R Vedantam, S Scherer, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | | 2023 |
Devi ParikhGeorgia Tech, Facebook AI ResearchVerified email at gatech.edu
