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Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks.
Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation
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Imagenet classification with deep convolutional neural networks
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Microsoft coco: Common objects in context
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
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Faster r-cnn: Towards real-time object detection with region proposal networks
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Apac: Augmented pattern classification with neural networks
Ikuro Sato, Hiroki Nishimura, and Kensuke Yokoi · 2015
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Training deep nets with sublinear memory cost
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Sgdr: Stochastic gradient descent with warm restarts
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Yfcc100m: The new data in multimedia research
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Mixed precision training
Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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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
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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
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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
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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, et al · 2020
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Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
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M5product: A multi-modal pretraining benchmark for e-commercial product downstream tasks
Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Xiaoyong Wei, Minlong Lu, and Xiaodan Liang · 2021
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Seeing out of the box: End-to-end pre-training for vision-language representation learning
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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, Yunhsuan Sung, Zhen Li, and Tom Duerig · 2021
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Uniter: Universal image-text representation learning
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An image is worth 16x16 words: Transformers for image recognition at scale
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Large-scale adversarial training for vision-and-language representation learning
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Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler, and Daniel Soudry · 2020
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