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We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs).
Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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Textcaps: a dataset for image captioning with reading comprehension
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Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers
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Learning transferable visual models from natural language supervision
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Learn to explain: Multimodal reasoning via thought chains for science question answering
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, et al · 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models
Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji · 2023
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Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, et al · 2023
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Prismatic vlms: Investigating the design space of visually-conditioned language models
Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, and Dorsa Sadigh · 2024
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2024
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Mm1: Methods, analysis & insights from multimodal llm pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, et al · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
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Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, et al · 2023
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Mm-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang · 2023
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Multimodal foundation models: From specialists to general-purpose assistants
Chunyuan Li, Zhe Gan, Zhengyuan Yang, Jianwei Yang, Linjie Li, Lijuan Wang, and Jianfeng Gao
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Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen
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Eyes wide shut? exploring the visual shortcomings of multimodal llms, 2024
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie · 2024
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Llava-phi: Efficient multi-modal assistant with small language model
Yichen Zhu, Minjie Zhu, Ning Liu, Zhicai Ou, Xiaofeng Mou, and Jian Tang · 2024
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