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Brian (Bo) Li
Brian (Bo) Li
PhD Student, MMLab@NTU, Singapore
Подтвержден адрес электронной почты в домене e.ntu.edu.sg - Главная страница
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Год
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
11312024
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
7802024
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
7472024
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
707*2023
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
3512020
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
3152022
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
2522024
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
2052019
Video instruction tuning with synthetic data
Y Zhang, J Wu, W Li, B Li, Z Ma, Z Liu, C Li
arXiv preprint arXiv:2410.02713, 2024
1592024
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
1532020
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
1382021
Self-Supervised Pretraining Improves Self-Supervised Pretraining
CJ Reed, X Yue, A Nrusimha, S Ebrahimi, V Vijaykumar, R Mao, B Li, ...
WACV 2022, 2021
1322021
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
1142020
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
1032020
Sparse Mixture-of-Experts are Domain Generalizable Learners
B Li, J Yang, J Ren, Y Wang, Z Liu
ICLR 2023 (Oral), 2022
912022
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
882024
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
822021
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Y Wang, B Li, T Che, K Zhou, D Li, Z Liu
ICCV 2021, 2021
752021
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
702020
LLaVA-next: improved reasoning, OCR, and world knowledge (2024)
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 2 (5), 6, 2024
672024
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