Li, Chunyuan

Improved Baselines with Visual Instruction Tuning

Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 1,748

Improved Baselines with Visual Instruction Tuning

Liu, H., Li, C., Li, Y., & Lee, Y. J. (2023) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 1,733

A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 921

Visual Instruction Tuning

Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 682

Florence: A New Foundation Model for Computer Vision

Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al. (2021) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 389

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training

Hao, W., Li, C., Li, X., Carin, L., & Gao, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 264

Instruction Tuning with GPT-4

Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 183

OCRBench: on the hidden mystery of OCR in large multimodal models

Liu, Y., Li, Z., Huang, M., Yang, B., Yu, W., Li, C., Yin, X.-C., Liu, C.-L., Jin, L., & Bai, X. (2024) · Science China Information Sciences

Handwritten Text Recognition TechniquesMultimodal Machine Learning Applications

Cited by 167

Vision-Language Pre-Training: Basics, Recent Advances, and Future Trends

Gan, Z., Li, L., Li, C., Wang, L., Liu, Z., & Gao, J. (2022) · Foundations and Trends® in Computer Graphics and Vision

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 155

LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models

Zhang, K., Li, B., Zhang, P., Pu, F., Cahyono, J. A., Hu, K., Liu, S., Zhang, Y., Yang, J., Li, C., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 45

LLaVA-OneVision: Easy Visual Task Transfer

Li, B., Zhang, Y., Guo, D., Zhang, R., Li, F., Zhang, H., Zhang, K., Zhang, P., Li, Y., Liu, Z., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 44

LAFITE: Towards Language-Free Training for Text-to-Image Generation

Zhou, Y., Zhang, R., Chen, C., Li, C., Tensmeyer, C., Yu, T., Gu, J., Xu, J., & Sun, T. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 38

Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

Yang, J., Zhang, H., Li, F., Zou, X., Li, C., & Gao, J. (2023) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 25

MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts

Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., & Gao, J. (2023) · arXiv

Multimodal Machine Learning ApplicationsMathematics, Computing, and Information Processing

Cited by 12