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We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings.
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Sahar Kazemzadeh, Vicente Ordonez, Marc andre Matten, and Tamara L. Berg · 2014
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Textcaps: a dataset for image captioning with reading comprehension
Oleksii Sidorov, Ronghang Hu, Marcus Rohrbach, and Amanpreet Singh · 2020
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Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei Zhang, Zhou Yu, Xiaodan Liang, and Song-Chun Zhu · 2021
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Learning transferable visual models from natural language supervision
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High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
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Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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Training language models to follow instructions with human feedback
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