EVA: Exploring the limits of masked visual representation learning at scale Y Fang, W Wang, B Xie, Q Sun, L Wu, X Wang, T Huang, X Wang, Y Cao CVPR 2023, Highlight, 2023 | 348 | 2023 |
EVA-CLIP: Improved training techniques for CLIP at scale Q Sun, Y Fang, L Wu, X Wang, Y Cao arXiv preprint arXiv:2303.15389, 2023 | 142 | 2023 |
EVA-02: A visual representation for neon genesis Y Fang, Q Sun, X Wang, T Huang, X Wang, Y Cao arXiv preprint arXiv:2303.11331, 2023 | 87 | 2023 |
Emu: Generative Pretraining in Multimodality Q Sun, Q Yu, Y Cui, F Zhang, X Zhang, Y Wang, H Gao, J Liu, T Huang, ... ICLR 2024, 2023 | 73* | 2023 |
The multi-agent behavior dataset: Mouse dyadic social interactions JJ Sun, T Karigo, D Chakraborty, SP Mohanty, B Wild, Q Sun, C Chen, ... NeurIPS 2021, 2021 | 36 | 2021 |
Generative multimodal models are in-context learners Q Sun, Y Cui, X Zhang, F Zhang, Q Yu, Z Luo, Y Wang, Y Rao, J Liu, ... CVPR 2024, 2023 | 31 | 2023 |
Capsfusion: Rethinking image-text data at scale Q Yu, Q Sun, X Zhang, Y Cui, F Zhang, X Wang, J Liu CVPR 2024, 2023 | 7 | 2023 |
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters Q Sun, J Wang, Q Yu, Y Cui, F Zhang, X Zhang, X Wang arXiv preprint arXiv:2402.04252, 2024 | 4 | 2024 |