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Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can parse natural queries about the visual content and generate human-like outputs.
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019 · 1908
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Im2text: Describing images using 1 million captioned photographs
Vicente Ordonez, Girish Kulkarni, and Tamara L. Berg. 2011 · 2011
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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 · 2014
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Abductive reasoning
Douglas Walton. 2014 · 2014
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018 · 2018
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Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019 · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
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Sunny and dark outside?! improving answer consistency in VQA through entailed question generation
Arijit Ray, Karan Sikka, Ajay Divakaran, Stefan Lee, and Giedrius Burachas. 2019 · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 2019
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Pixel-bert: Aligning image pixels with text by deep multi-modal transformers
Zhicheng Huang, Zhaoyang Zeng, Bei Liu, Dongmei Fu, and Jianlong Fu. 2020 · 2020
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Inconsistencies in crowdsourced slot-filling annotations: A typology and identification methods
Stefan Larson, Adrian Cheung, Anish Mahendran, Kevin Leach, and Jonathan K. Kummerfeld. 2020 · 2020
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Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training
Gen Li, Nan Duan, Yuejian Fang, Ming Gong, and Daxin Jiang. 2020a · 2020
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Oscar: Object-semantics aligned pre-training for vision-language tasks
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, and Jianfeng Gao. 2020b · 2020
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Squinting at VQA models: Introspecting VQA models with sub-questions
Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh, Eric Horvitz, Marco Túlio Ribeiro, Besmira Nushi, and Ece Kamar. 2020 · 2020
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Learning from multiple inconsistent and dependent annotators to support classification tasks
Julián Gil González, Álvaro-Ángel Orozco-Gutierrez, and Andrés Álvarez-Meza. 2021 · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Seeing out of the box: End-to-end pre-training for vision-language representation learning
Zhicheng Huang, Zhaoyang Zeng, Yupan Huang, Bei Liu, Dongmei Fu, and Jianlong Fu. 2021 · 2021
Earlier work this paper cites.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
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Vilt: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021 · 2021
Earlier work this paper cites.
Unsupervised vision-and-language pre-training without parallel images and captions
Liunian Harold Li, Haoxuan You, Zhecan Wang, Alireza Zareian, Shih-Fu Chang, and Kai-Wei Chang. 2021b · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021
Cited alongside, same era.
Simvlm: Simple visual language model pretraining with weak supervision
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. 2021 · 2021
Cited alongside, same era.
E2E-VLP: end-to-end vision-language pre-training enhanced by visual learning
Haiyang Xu, Ming Yan, Chenliang Li, Bin Bi, Songfang Huang, Wenming Xiao, and Fei Huang. 2021 · 2021
Cited alongside, same era.
Vinvl: Revisiting visual representations in vision-language models
Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. 2021 · 2021
PEVL: position-enhanced pre-training and prompt tuning for vision-language models
Yuan Yao, Qianyu Chen, Ao Zhang, Wei Ji, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. 2022 · 2022
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A categorical archive of chatgpt failures
Ali Borji. 2023 · 2023
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Sparks of artificial general intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott M. Lundberg, Harsha Nori, Hamid Palangi, Marco Túlio Ribeiro, and Yi Zhang. 2023 · 2023
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Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, and Ajay Divakaran. 2023 · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven C. H. Hoi. 2023 · 2023
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Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2022 · 2022
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An empirical study of training end-to-end vision-and-language transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. 2022 · 2022
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Vision-language pre-training: Basics, recent advances, and future trends
Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, and Jianfeng Gao. 2022 · 2022
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The abduction of sherlock holmes: A dataset for visual abductive reasoning
Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, and Yejin Choi. 2022 · 2022
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Maintaining reasoning consistency in compositional visual question answering
Chenchen Jing, Yunde Jia, Yuwei Wu, Xinyu Liu, and Qi Wu. 2022 · 2022
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In-context reinforcement learning with algorithm distillation
Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan Brooks, et al. 2022 · 2022
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Text and patterns: For effective chain of thought, it takes two to tango
Aman Madaan and Amir Yazdanbakhsh. 2022 · 2022
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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, Zhenyu Qiu, Wei Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, and Rongrong Ji. 2023 · 2023
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Tab-cot: Zero-shot tabular chain of thought
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Measuring faithfulness in chain-of-thought reasoning
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Symbolic chain-of-thought distillation: Small models can also "think" step-by-step
Liunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang, and Yejin Choi. 2023b · 2023
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GPT-4 technical report
OpenAI. 2023 · 2023
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Certified reasoning with language models
Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, and Noah D. Goodman. 2023 · 2023
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A survey of evaluation metrics used for NLG systems
Ananya B. Sai, Akash Kumar Mohankumar, and Mitesh M. Khapra. 2023 · 2023
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He. 2023 · 2023
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Testing the general deductive reasoning capacity of large language models using OOD examples
Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, and He He. 2023 · 2023
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Zeroshotdataaug: Generating and augmenting training data with chatgpt
Solomon Ubani, Suleyman Olcay Polat, and Rodney Nielsen. 2023 · 2023
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Enhancing human-like multi-modal reasoning: A new challenging dataset and comprehensive framework
Jingxuan Wei, Cheng Tan, Zhangyang Gao, Linzhuang Sun, Siyuan Li, Bihui Yu, Ruifeng Guo, and Stan Z. Li. 2023 · 2023
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Lvlm-ehub: A comprehensive evaluation benchmark for large vision-language models
Peng Xu, Wenqi Shao, Kaipeng Zhang, Peng Gao, Shuo Liu, Meng Lei, Fanqing Meng, Siyuan Huang, Yu Qiao, and Ping Luo. 2023 · 2023
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Bridge the gap between CV and nlp! A gradient-based textual adversarial attack framework
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
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Executable code actions elicit better llm agents
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