| Mmbench: Is your multi-modal model an all-around player? Y Liu, H Duan, Y Zhang, B Li, S Zhang, W Zhao, Y Yuan, J Wang, C He, ... European conference on computer vision, 216-233, 2024 | 720 | 2024 |
| MIMIC-IT: Multi-modal in-context instruction tuning B Li, Y Zhang, L Chen, J Wang, F Pu, J Yang, C Li, Z Liu arXiv preprint arXiv:2306.05425, 2023 | 637 | 2023 |
| Llava-next: Improved reasoning, ocr, and world knowledge H Liu, C Li, Y Li, B Li, Y Zhang, S Shen, YJ Lee URL https://llava-vl. github. io/blog/2024-01-30-llava-next, 2024 | 460* | 2024 |
| A review of single-source deep unsupervised visual domain adaptation S Zhao, X Yue, S Zhang, B Li, H Zhao, B Wu, R Krishna, JE Gonzalez, ... IEEE Transactions on Neural Networks and Learning Systems 33 (2), 473-493, 2020 | 318 | 2020 |
| Llava-onevision: Easy visual task transfer B Li, Y Zhang, D Guo, R Zhang, F Li, H Zhang, K Zhang, P Zhang, Y Li, ... arXiv preprint arXiv:2408.03326, 2024 | 248 | 2024 |
| OpenOOD: Benchmarking Generalized Out-of-Distribution Detection J Yang, P Wang, D Zou, Z Zhou, K Ding, W Peng, H Wang, G Chen, B Li, ... NeurIPS 2022 Datasets and Benchmarks Track, 2022 | 240 | 2022 |
| Multi-source domain adaptation for semantic segmentation S Zhao, B Li, X Yue, Y Gu, P Xu, R Hu, H Chai, K Keutzer NeurIPS 2019, 2019 | 200 | 2019 |
| Multi-source domain adaptation in the deep learning era: A systematic survey S Zhao, B Li, P Xu, K Keutzer arXiv preprint arXiv:2002.12169, 2020 | 144 | 2020 |
| Self-Supervised Pretraining Improves Self-Supervised Pretraining CJ Reed, X Yue, A Nrusimha, S Ebrahimi, V Vijaykumar, R Mao, B Li, ... WACV 2022, 2021 | 125 | 2021 |
| Invariant Information Bottleneck for Domain Generalization B Li, Y Shen, Y Wang, W Zhu, CJ Reed, D Li, K Keutzer, H Zhao AAAI 2022, 2021 | 110 | 2021 |
| Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation B Li, Y Wang, S Zhang, D Li, T Darrell, K Keutzer, H Zhao CVPR 2021, 2020 | 103 | 2020 |
| Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models F Li, R Zhang, H Zhang, Y Zhang, B Li, W Li, Z Ma, C Li arXiv preprint arXiv:2407.07895, 2024 | 100 | 2024 |
| ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation S Zhao, Y Wang, B Li, B Wu, Y Gao, P Xu, T Darrell, K Keutzer AAAI 2021, 2020 | 96 | 2020 |
| MADAN: Multi-source adversarial domain aggregation network for domain adaptation S Zhao, B Li, P Xu, X Yue, G Ding, K Keutzer International Journal of Computer Vision 129 (8), 2399-2424, 2021 | 75 | 2021 |
| Energy-Based Open-World Uncertainty Modeling for Confidence Calibration Y Wang, B Li, T Che, K Zhou, D Li, Z Liu ICCV 2021, 2021 | 66 | 2021 |
| Sparse Mixture-of-Experts are Domain Generalizable Learners B Li, J Yang, J Ren, Y Wang, Z Liu ICLR 2023 (Oral), 2022 | 64 | 2022 |
| Rethinking distributional matching based domain adaptation B Li, Y Wang, T Che, S Zhang, S Zhao, P Xu, W Zhou, Y Bengio, ... arXiv preprint arXiv:2006.13352, 2020 | 63 | 2020 |
| Octopus: Embodied vision-language programmer from environmental feedback J Yang, Y Dong, S Liu, B Li, Z Wang, H Tan, C Jiang, J Kang, Y Zhang, ... European Conference on Computer Vision, 20-38, 2024 | 50 | 2024 |
| Llava-next: Stronger llms supercharge multimodal capabilities in the wild B Li, K Zhang, H Zhang, D Guo, R Zhang, F Li, Y Zhang, Z Liu, C Li May, 2024 | 50 | 2024 |
| Otterhd: A high-resolution multi-modality model B Li, P Zhang, J Yang, Y Zhang, F Pu, Z Liu arXiv preprint arXiv:2311.04219, 2023 | 44 | 2023 |
Ziwei LiuAssistant Professor, Nanyang Technological University在 ntu.edu.sg 的电子邮件经过验证