| 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 | 776 | 2019 |
| Once for all: Train one network and specialize it for efficient deployment H Cai, C Gan, T Wang, Z Zhang, S Han ICLR, 2020 | 504 | 2020 |
| 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 | 383 | 2017 |
| 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, 2019 | 362 | 2019 |
| 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 | 340 | 2018 |
| Neural-symbolic vqa: Disentangling reasoning from vision and language understanding K Yi, J Wu, C Gan, A Torralba, P Kohli, JB Tenenbaum NeurIPS, 2018 | 326 | 2018 |
| 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 | 319 | 2015 |
| 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 | 252 | 2019 |
| 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 | 228 | 2017 |
| Learning attributes equals multi-source domain generalization C Gan, T Yang, B Gong Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 189 | 2016 |
| Recurrent topic-transition GAN for visual paragraph generation X Liang, Z Hu, H Zhang, C Gan, EP Xing ICCV 2017, 2017 | 183 | 2017 |
| 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 | 182 | 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 | 176 | 2018 |
| Clevrer: Collision events for video representation and reasoning K Yi, C Gan, Y Li, P Kohli, J Wu, A Torralba, JB Tenenbaum ICLR, 2020, 2020 | 154 | 2020 |
| The sound of motions H Zhao, C Gan, WC Ma, A Torralba Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 142 | 2019 |
| Defensive quantization: When efficiency meets robustness J Lin, C Gan, S Han ICLR, 2019, 2019 | 136 | 2019 |
| 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 | 130 | 2019 |
| You lead, we exceed: Labor-free video concept learning by jointly exploiting web videos and images C Gan, T Yao, K Yang, Y Yang, T Mei Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 113 | 2016 |
| Mcunet: Tiny deep learning on iot devices J Lin, WM Chen, Y Lin, J Cohn, C Gan, S Han NeurIPS, 2020 | 108 | 2020 |
| Music gesture for visual sound separation C Gan, D Huang, H Zhao, JB Tenenbaum, A Torralba Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 100 | 2020 |
Joshua B. TenenbaumMITVerified email at mit.edu
