| Tsm: Temporal shift module for efficient video understanding J Lin, C Gan, S Han Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 1642 | 2019 |
| Once-for-all: Train one network and specialize it for efficient deployment H Cai, C Gan, T Wang, Z Zhang, S Han arXiv preprint arXiv:1908.09791, 2019 | 1114 | 2019 |
| The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision J Mao, C Gan, P Kohli, JB Tenenbaum, J Wu ICLR, 2019 | 668 | 2019 |
| Neural-symbolic vqa: Disentangling reasoning from vision and language understanding K Yi, J Wu, C Gan, A Torralba, P Kohli, JB Tenenbaum NeurIPS, 2018 | 588 | 2018 |
| The sound of pixels H Zhao, C Gan, A Rouditchenko, C Vondrick, J McDermott, A Torralba Proceedings of the European conference on computer vision (ECCV), 570-586, 2018 | 516 | 2018 |
| Semantic compositional networks for visual captioning Z Gan, C Gan, X He, Y Pu, K Tran, J Gao, L Carin, L Deng Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 495 | 2017 |
| Graph convolutional networks for temporal action localization R Zeng, W Huang, M Tan, Y Rong, P Zhao, J Huang, C Gan Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 465 | 2019 |
| Devnet: A deep event network for multimedia event detection and evidence recounting C Gan, N Wang, Y Yang, DY Yeung, AG Hauptmann Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 379 | 2015 |
| Mcunet: Tiny deep learning on iot devices J Lin, WM Chen, Y Lin, J Cohn, C Gan, S Han NeurIPS, 2020 | 351 | 2020 |
| CLEVRER: CoLlision Events for Video REpresentation and Reasoning K Yi, C Gan, Y Li, P Kohli, J Wu, A Torralba, JB Tenenbaum ICLR, 2020 | 349 | 2020 |
| StyleNet: Generating Attractive Visual Captions with Styles C Gan, Z Gan, X He, J Gao, L Deng Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017 | 340 | 2017 |
| Beyond rnns: Positional self-attention with co-attention for video question answering X Li, J Song, L Gao, X Liu, W Huang, X He, C Gan Proceedings of the AAAI conference on artificial intelligence 33 (01), 8658-8665, 2019 | 252 | 2019 |
| The sound of motions H Zhao, C Gan, WC Ma, A Torralba Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 250 | 2019 |
| Attention clusters: Purely attention based local feature integration for video classification X Long, C Gan, G De Melo, J Wu, X Liu, S Wen Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 241 | 2018 |
| End-to-End Learning of Motion Representation for Video Understanding L Fan, W Huang, C Gan, S Ermon, B Gong, J Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 234 | 2018 |
| Recurrent topic-transition GAN for visual paragraph generation X Liang, Z Hu, H Zhang, C Gan, EP Xing ICCV, 2017 | 233 | 2017 |
| Hat: Hardware-aware transformers for efficient natural language processing H Wang, Z Wu, Z Liu, H Cai, L Zhu, C Gan, S Han ACL, 2020 | 229 | 2020 |
| Learning attributes equals multi-source domain generalization C Gan, T Yang, B Gong Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 226 | 2016 |
| Dense regression network for video grounding R Zeng, H Xu, W Huang, P Chen, M Tan, C Gan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 223 | 2020 |
| Tinytl: Reduce memory, not parameters for efficient on-device learning H Cai, C Gan, L Zhu, S Han Advances in Neural Information Processing Systems 33, 11285-11297, 2020 | 220* | 2020 |
Joshua B. TenenbaumMITVerified email at mit.edu
