| Few-shot learning via embedding adaptation with set-to-set functions HJ Ye, H Hu, DC Zhan, F Sha Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 789* | 2020 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... Technical Report, 2023 | 419 | 2023 |
| Compressed Video Action Recognition CY Wu, M Zaheer, H Hu, R Manmatha, AJ Smola, P Krähenbühl Computer Vision and Pattern Recognition (CVPR), 2018 Proceedings of …, 2017 | 380 | 2017 |
| Structure inference machines: Recurrent neural networks for analyzing relations in group activity recognition Z Deng, A Vahdat, H Hu, G Mori Computer Vision and Pattern Recognition (CVPR), 2016 Proceedings of IEEE …, 2016 | 285 | 2016 |
| Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation R Vuorio, SH Sun, H Hu, JJ Lim Advances in Neural Information Processing Systems (NeurIPS) 2019, 2019 | 273* | 2019 |
| Learning the best pooling strategy for visual semantic embedding J Chen, H Hu, H Wu, Y Jiang, C Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 184 | 2021 |
| Engaging image captioning via personality K Shuster, S Humeau, H Hu, A Bordes, J Weston Computer Vision and Pattern Recognition (CVPR), 2019 Proceedings of IEEE …, 2018 | 177 | 2018 |
| Learning structured inference neural networks with label relations H Hu, GT Zhou, Z Deng, Z Liao, G Mori Computer Vision and Pattern Recognition (CVPR), 2016 Proceedings of IEEE …, 2016 | 173* | 2016 |
| Cross-Modal and Hierarchical Modeling of Video and Text B Zhang, H Hu, F Sha Proceedings of the European Conference on Computer Vision (ECCV), 2018 | 145 | 2018 |
| Pix2Struct: Screenshot parsing as pretraining for visual language understanding K Lee, M Joshi, I Turc, H Hu, F Liu, J Eisenschlos, U Khandelwal, P Shaw, ... ICML 2023, 2023 | 111 | 2023 |
| Re-imagen: Retrieval-augmented text-to-image generator W Chen, H Hu, C Saharia, WW Cohen ICLR 2023, 2022 | 92 | 2022 |
| Multi-Task Learning for Sequence Tagging: An Empirical Study S Changpinyo, H Hu, F Sha Proceedings of the International Conference on Computational Linguistics …, 2018 | 82 | 2018 |
| Pali-x: On scaling up a multilingual vision and language model X Chen, J Djolonga, P Padlewski, B Mustafa, S Changpinyo, J Wu, ... Preprints, 2023 | 80 | 2023 |
| Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning HJ Ye, H Hu, DC Zhan International Journal of Computer Vision, 2021 | 71 | 2021 |
| BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps W Zhu, H Hu, J Chen, Z Deng, V Jain, E Ie, F Sha ACL 2020, 2020 | 69 | 2020 |
| Subject-driven text-to-image generation via apprenticeship learning W Chen, H Hu, Y Li, N Ruiz, X Jia, MW Chang, WW Cohen NeurIPS 2024 36, 2024 | 63 | 2024 |
| Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets WL Chao, H Hu, F Sha The North American Chapter of the Association for Computational Linguistics …, 2018 | 57* | 2018 |
| Cross-Dataset Adaptation for Visual Question Answering WL Chao, H Hu, F Sha Computer Vision and Pattern Recognition (CVPR), 2018 Proceedings of IEEE …, 2018 | 54 | 2018 |
| On Model Calibration for Long-Tailed Object Detection and Instance Segmentation TY Pan, C Zhang, Y Li, H Hu, D Xuan, S Changpinyo, B Gong, WL Chao NeurIPS 2021, 2021 | 40 | 2021 |
| Learning Answer Embeddings for Visual Question Answering H Hu, WL Chao, F Sha Computer Vision and Pattern Recognition (CVPR), 2018 Proceedings of IEEE …, 2018 | 40 | 2018 |
