International Journal of Computer Vision, 127:398-414, 2016.
[2] Zaid Khan, B Vijaykumar, Samuel Schulter, Xiang Yu, Yun Raymond Fu, and Manmohan Chandraker. Q: How to specialize large vision-language models to data-scarce vqa tasks? a: Self-train on unlabeled images! In CVPR, 2023.
[3] Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra. Vqa: Visual question answering. International Journal of Computer Vision, 123:4 - 31, 2015. URL https://api_semanticscholar.org/CorpusID:3180429
[4] Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom. Augmented language models: a survey. ArXiv, abs/2302.07842, 2023.
[5] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In NeurIPS, 2022.
[6] Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In ICLR, 2023.
[7] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. ArXiv, abs/2301.12597, 2023.
[8] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning. ArXiv, abs/2304.08485, 2023.
[9] Deyao Zhu, Jun Chen, Xiaogian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. ArXiv, abs/2304.10592, 2023.
[10] Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, and Lijuan Wang. An empirical study of gpt-3 for few-shot knowledge-based vqa. In AAAI, 2021.
[11] Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, Dacheng Tao, and Steven Hoi. From images to textual prompts: Zero-shot visual question answering with frozen large language models. In CVPR, 2023.
[12] Ethan Perez, Patrick Lewis, Wen tau Yih, Kyunghyun Cho, and Douwe Kiela. Unsupervised question decomposition for question answering. In EMNLP, 2020.
[13] Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal. Text modular networks: Learning to decompose tasks in the language of existing models. NAACL-HLT, 2021.
[14] Pruthvi H. Patel, Swaroop Mishra, Mihir Parmar, and Chitta Baral. Is a question decomposition unit all we need? In EMNLP, 2022.
[15] Constantin Eichenberg, Sidney Black, Samuel Weinbach, Letitia Parcalabescu, and Anette Frank. MAGMA – multimodal augmentation of generative models through adapter-based finetuning. In EMNLP Findings, 2022.
[16] Jing Yu Koh, Ruslan Salakhutdinov, and Daniel Fried. Grounding language models to images for multimodal inputs and outputs. In ICML, 2023.
[17] Stephanie Chan, Adam Santoro, Andrew K. Lampinen, Jane Wang, Aaditya Singh, Pierre H. Richemond, James L. McClelland, and Felix Hill. Data distributional properties drive emergent in-context learning in transformers. In NeurIPS, 2022.
[18] Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. Rethinking the role of demonstrations: What makes in-context learning work? In EMNLP, 2022.