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The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts.
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Dai, W., Li, J., Li, D., Tiong, A.M.H., Zhao, J., Wang, W., Li, B., Fung, P., Hoi, S.: Instructblip: Towards general-purpose vision-language models with instruction tuning (2023)
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Ye, Q., Xu, H., Xu, G., Ye, J., Yan, M., Zhou, Y., Wang, J., Hu, A., Shi, P., Shi, Y., Jiang, C., Li, C., Xu, Y., Chen, H., Tian, J., Qian, Q., Zhang, J., Huang, F.: mplug-owl: Modularization empowers large language models with multimodality (2023)
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Ye, Q., Xu, H., Ye, J., Yan, M., Hu, A., Liu, H., Qian, Q., Zhang, J., Huang, F., Zhou, J.: mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration (2023)
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Zhang, R., Hu, X., Li, B., Huang, S., Deng, H., Li, H., Qiao, Y., Gao, P.: Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners. CVPR 2023 (2023)
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2024
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Li, B., Zhang, K., Zhang, H., Guo, D., Zhang, R., Li, F., Zhang, Y., Liu, Z., Li, C.: Llava-next: Stronger llms supercharge multimodal capabilities in the wild. https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/ (2024)
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Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., Lee, Y.J.: Llava-next: Improved reasoning, ocr, and world knowledge (January 2024), https://llava-vl.github.io/blog/2024-01-30-llava-next/
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Sun, K., Pan, J., Ge, Y., Li, H., Duan, H., Wu, X., Zhang, R., Zhou, A., Qin, Z., Wang, Y., et al.: Journeydb: A benchmark for generative image understanding. Advances in Neural Information Processing Systems 36
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Wang, K., Ren, H., Zhou, A., Lu, Z., Luo, S., Shi, W., Zhang, R., Song, L., Zhan, M., Li, H.: Mathcoder: Seamless code integration in LLMs for enhanced mathematical reasoning. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=z8TW0ttBPp
2024
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Zhang, R., Han, J., Zhou, A., Hu, X., Yan, S., Lu, P., Li, H., Gao, P., Qiao, Y.: LLaMA-adapter: Efficient fine-tuning of large language models with zero-initialized attention. In: The Twelfth International Conference on Learning Representations (2024), https://openreview.net/forum?id=d4UiXAHN2W
2024
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