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CLIP has shown a remarkable zero-shot capability on a wide range of vision tasks.
Visual entailment: A novel task for fine-grained image understanding
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The open images dataset v4
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Language models are few-shot learners
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
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Vqa: Visual question answering
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A large annotated corpus for learning natural language inference
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Deep residual learning for image recognition
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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. 2017 · 2017
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Attention is all you need
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Transforming question answering datasets into natural language inference datasets
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Improving language understanding by generative pre-training
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Beto, bentz, becas: The surprising cross-lingual effectiveness of bert
Shijie Wu and Mark Dredze. 2019 · 2019
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Deep modular co-attention networks for visual question answering
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
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Making pre-trained language models better few-shot learners
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Scaling up visual and vision-language representation learning with noisy text supervision
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Vilt: Vision-and-language transformer without convolution or region supervision
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Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. 2019 · 2019
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Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020 · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020 · 2020
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2020
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Stanza: A Python natural language processing toolkit for many human languages
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Eliciting knowledge from language models using automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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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
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
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How much can clip benefit vision-and-language tasks?
Sheng Shen, Liunian Harold Li, Hao Tan, Mohit Bansal, Anna Rohrbach, Kai-Wei Chang, Zhewei Yao, and Kurt Keutzer. 2021 · 2021
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Multimodal few-shot learning with frozen language models
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
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Vlmo: Unified vision-language pre-training with mixture-of-modality-experts
Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. 2021 · 2021
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mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
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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
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