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Recent breakthroughs in vision-language models (VLMs) emphasize the necessity of benchmarking human preferences in real-world multimodal interactions.
Rank analysis of incomplete block designs: I. the method of paired comparisons
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Visualbert: A simple and performant baseline for vision and language, 2019
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
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Anand Mishra, Shashank Shekhar, Ajeet Kumar Singh, and Anirban Chakraborty · 2019
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Oscar: Object-semantics aligned pre-training for vision-language tasks
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Textcaps: a dataset for image captioning with reading comprehension
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Docvqa: A dataset for vqa on document images
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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Learning transferable visual models from natural language supervision
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Recipes for safety in open-domain chatbots, 2021
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