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Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in diverse tasks across different domains, with an increasing focus on improving their zero-shot generalization capabilities for unseen multimodal tasks.
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Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation
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Xiang Lisa Li and Percy Liang. 2021 · 2021
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning
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PaLM-e: An embodied multimodal language model
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Mme: A comprehensive evaluation benchmark for multimodal large language models
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mplug-owl: Modularization empowers large language models with multimodality
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Investigating the catastrophic forgetting in multimodal large language models
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Svit: Scaling up visual instruction tuning
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
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