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Visual question answering (VQA) has traditionally been treated as a single-step task where each question receives the same amount of effort, unlike natural human question-answering strategies.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Vqa: Visual question answering
Aishwarya Agrawal, Jiasen Lu, Stanislaw Antol, Margaret Mitchell, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra · 2015
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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 · 2016
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A dataset of clinically generated visual questions and answers about radiology images
Jason J. Lau, Soumya Gayen, Asma Ben Abacha, and Dina Demner-Fushman · 2018
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Cycle-consistency for robust visual question answering
Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew · 2019
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen tau Yih, Kyunghyun Cho, and Douwe Kiela · 2020
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Pathvqa: 30000+ questions for medical visual question answering
Xuehai He, Yichen Zhang, Luntian Mou, Eric Xing, and Pengtao Xie · 2020
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Vqa-lol: Visual question answering under the lens of logic
Tejas Gokhale, Pratyay Banerjee, Chitta Baral, and Yezhou Yang · 2020
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Squinting at vqa models: Introspecting vqa models with sub-questions
R. R. Selvaraju, P. Tendulkar, D. Parikh, E. Horvitz, M. Tulio Ribeiro, B. Nushi, and E. Kamar · 2020
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A dataset and baselines for visual question answering on art
Noa Garcia, Chentao Ye, Zihua Liu, Qingtao Hu, Mayu Otani, Chenhui Chu, Yuta Nakashima, and Teruko Mitamura · 2020
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Weaqa: Weak supervision via captions for visual question answering
Pratyay Banerjee, Tejas Gokhale, Yezhou Yang, and Chitta Baral · 2020
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An empirical study of gpt-3 for few-shot knowledge-based vqa
Zhengyuan Yang, Zhe Gan, Jianfeng Wang, Xiaowei Hu, Yumao Lu, Zicheng Liu, and Lijuan Wang · 2021
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Text modular networks: Learning to decompose tasks in the language of existing models
Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 2021
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Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering
Bo Liu, Li-Ming Zhan, Li Xu, Lin Ma, Yan Fang Yang, and Xiao-Ming Wu · 2021
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Artificial Intelligence: A Modern Approach , chapter 2, page 39
Stewart Russel and Peter Norvig · 2021
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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven C. H. Hoi · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, and Felix Hill · 2021
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Is a question decomposition unit all we need?
Pruthvi H. Patel, Swaroop Mishra, Mihir Parmar, and Chitta Baral · 2022
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MAGMA – multimodal augmentation of generative models through adapter-based finetuning
Constantin Eichenberg, Sidney Black, Samuel Weinbach, Letitia Parcalabescu, and Anette Frank · 2022
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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew K. Lampinen, Jane Wang, Aaditya Singh, Pierre H. Richemond, James L. McClelland, and Felix Hill · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, and Zhiting Hu · 2022
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Augmented language models: a survey
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 · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Winoground: Probing vision and language models for visio-linguistic compositionality
Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, and Candace Ross · 2022
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt · 2022
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Single-stream multi-level alignment for vision-language pretraining
Zaid Khan, B Vijaykumar, Xiang Yu, Samuel Schulter, Manmohan Chandraker, and Yun Raymond Fu · 2022
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Unified-io: A unified model for vision, language, and multi-modal tasks
Jiasen Lu, Christopher Clark, Rowan Zellers, Roozbeh Mottaghi, and Aniruddha Kembhavi · 2022
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Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework
Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, S. Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Wei Yu, Vincent Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed Huai hsin Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei · 2022
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A-okvqa: A benchmark for visual question answering using world knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi · 2022
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Grounding language models to images for multimodal inputs and outputs
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Least-to-most prompting enables complex reasoning in large language models
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Decomposed prompting: A modular approach for solving complex tasks
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Multimodal chain-of-thought reasoning in language models
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Llama: Open and efficient foundation language models
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Contrastive alignment of vision to language through parameter-efficient transfer learning
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Linearly mapping from image to text space
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Openflamingo: An open-source framework for training large autoregressive vision-language models
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Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
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