| Conformer: Convolution-augmented transformer for speech recognition A Gulati, J Qin, CC Chiu, N Parmar, Y Zhang, J Yu, W Han, S Wang, ... arXiv preprint arXiv:2005.08100, 2020 | 3554 | 2020 |
| Generative image inpainting with contextual attention J Yu, Z Lin, J Yang, X Shen, X Lu, TS Huang Computer Vision and Pattern Recognition (CVPR), 2018, 2018 | 3014 | 2018 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2532 | 2023 |
| Free-form image inpainting with gated convolution J Yu, Z Lin, J Yang, X Shen, X Lu, TS Huang International Conference on Computer Vision (ICCV), 2019, 2019 | 2252 | 2019 |
| Ntire 2017 challenge on single image super-resolution: Methods and results R Timofte, E Agustsson, L Van Gool, MH Yang, L Zhang, B Lim, S Son, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 2249 | 2017 |
| Unitbox: An advanced object detection network J Yu, Y Jiang, Z Wang, Z Cao, T Huang ACM International Conference on Multimedia (ACM MM), 2016, 2016 | 1789 | 2016 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1571 | 2023 |
| Coca: Contrastive captioners are image-text foundation models J Yu, Z Wang, V Vasudevan, L Yeung, M Seyedhosseini, Y Wu arXiv preprint arXiv:2205.01917, 2022 | 1446 | 2022 |
| Scaling autoregressive models for content-rich text-to-image generation J Yu, Y Xu, JY Koh, T Luong, G Baid, Z Wang, V Vasudevan, A Ku, Y Yang, ... arXiv preprint arXiv:2206.10789 2 (3), 5, 2022 | 1101 | 2022 |
| Simvlm: Simple visual language model pretraining with weak supervision Z Wang, J Yu, AW Yu, Z Dai, Y Tsvetkov, Y Cao arXiv preprint arXiv:2108.10904, 2021 | 871 | 2021 |
| Slimmable neural networks J Yu, L Yang, N Xu, J Yang, T Huang arXiv preprint arXiv:1812.08928, 2018 | 696 | 2018 |
| Wide activation for efficient and accurate image super-resolution J Yu, Y Fan, J Yang, N Xu, Z Wang, X Wang, T Huang BMVC 2019 (challenge report of the winning solution in NTIRE Challenge on …, 2018 | 540 | 2018 |
| Universally slimmable networks and improved training techniques J Yu, T Huang International Conference on Computer Vision (ICCV), 2019, 2019 | 479 | 2019 |
| Foreground-aware Image Inpainting W Xiong, J Yu, Z Lin, J Yang, X Lu, C Barnes, J Luo Computer Vision and Pattern Recognition (CVPR), 2019, 2019 | 472 | 2019 |
| Vector-quantized image modeling with improved vqgan J Yu, X Li, JY Koh, H Zhang, R Pang, J Qin, A Ku, Y Xu, J Baldridge, Y Wu arXiv preprint arXiv:2110.04627, 2021 | 462 | 2021 |
| Bignas: Scaling up neural architecture search with big single-stage models J Yu, P Jin, H Liu, G Bender, PJ Kindermans, M Tan, T Huang, X Song, ... European Conference on Computer Vision (ECCV), 2020, 2020 | 347 | 2020 |
| Contextnet: Improving convolutional neural networks for automatic speech recognition with global context W Han, Z Zhang, Y Zhang, J Yu, CC Chiu, J Qin, A Gulati, R Pang, Y Wu arXiv preprint arXiv:2005.03191, 2020 | 340 | 2020 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 312 | 2023 |
| AutoSlim: Towards One-Shot Architecture Search for Channel Numbers J Yu, T Huang NeurIPS 2019 Workshop on Energy Efficient Machine Learning and Cognitive …, 2019 | 267* | 2019 |
| others. 2023. Gemini: a family of highly capable multimodal models RA Gemini Team, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 0 | 249 | |
Yonghui WuGoogle BrainEmail yang diverifikasi di google.com