| Rise: Randomized input sampling for explanation of black-box models V Petsiuk, A Das, K Saenko arXiv preprint arXiv:1806.07421, 2018 | 755 | 2018 |
| R-C3D: Region Convolutional 3D Network for Temporal Activity Detection H Xu, A Das, K Saenko IEEE International Conference on Computer Vision, 5783-5792, 2017 | 747 | 2017 |
| Consistent re-identification in a camera network A Das, A Chakraborty, AK Roy-Chowdhury Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 220 | 2014 |
| Top-down visual saliency guided by captions V Ramanishka, A Das, J Zhang, K Saenko Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 154 | 2017 |
| Temporal model adaptation for person re-identification N Martinel, A Das, C Micheloni, AK Roy-Chowdhury Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 138 | 2016 |
| Multimodal video description V Ramanishka, A Das, DH Park, S Venugopalan, LA Hendricks, ... Proceedings of the 24th ACM international conference on Multimedia, 1092-1096, 2016 | 136 | 2016 |
| Weakly Supervised Summarization of Web Videos R Panda, A Das, Z Wu, J Ernst, AK Roy-Chowdhury International Conference on Computer Vision, 2017 | 90 | 2017 |
| Re-identification in the function space of feature warps N Martinel, A Das, C Micheloni, AK Roy-Chowdhury IEEE transactions on pattern analysis and machine intelligence 37 (8), 1656-1669, 2014 | 77 | 2014 |
| Semi-supervised action recognition with temporal contrastive learning A Singh, O Chakraborty, A Varshney, R Panda, R Feris, K Saenko, A Das Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 56 | 2021 |
| Network consistent data association A Chakraborty, A Das, AK Roy-Chowdhury IEEE transactions on pattern analysis and machine intelligence 38 (9), 1859-1871, 2015 | 37 | 2015 |
| Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks R Ahmad, MD Soltani, M Safari, A Srivastava, A Das IEEE Access 8, 132273-132284, 2020 | 35 | 2020 |
| Two-stream region convolutional 3D network for temporal activity detection H Xu, A Das, K Saenko IEEE transactions on pattern analysis and machine intelligence 41 (10), 2319 …, 2019 | 33 | 2019 |
| Contrast and mix: Temporal contrastive video domain adaptation with background mixing A Sahoo, R Shah, R Panda, K Saenko, A Das Advances in Neural Information Processing Systems 34, 23386-23400, 2021 | 28 | 2021 |
| Active Image Pair Selection for Continuous Person Re-identification A Das, R Panda, AK Roy-Chowdhury International Conference on Image Processing, 2015 | 27 | 2015 |
| Video summarization in a multi-view camera network R Panda, A Dasy, AK Roy-Chowdhury 2016 23rd International Conference on Pattern Recognition (ICPR), 2971-2976, 2016 | 24 | 2016 |
| Embedded Sparse Coding for Summarizing Multi-view Videos R Panda, A Das, AK Roy-Chowdhury International Conference on Image Processing, 2016 | 22 | 2016 |
| Continuous adaptation of multi-camera person identification models through sparse non-redundant representative selection A Das, R Panda, AK Roy-Chowdhury Computer Vision and Image Understanding 156, 66-78, 2017 | 12 | 2017 |
| Revisiting few-shot activity detection with class similarity control H Xu, X Sun, E Tzeng, A Das, K Saenko, T Darrell arXiv preprint arXiv:2004.00137, 2020 | 7 | 2020 |
| Select, label, and mix: Learning discriminative invariant feature representations for partial domain adaptation A Sahoo, R Panda, R Feris, K Saenko, A Das Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 6 | 2023 |
| Mitigating dataset imbalance via joint generation and classification A Sahoo, A Singh, R Panda, R Feris, A Das Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 6 | 2020 |
Kate SaenkoBoston UniversityVerified email at bu.edu
