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CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.
What the MASK? making sense of language-specific bert models
Debora Nozza, Federico Bianchi, and Dirk Hovy. 2020 · 2003
Earlier work this paper cites.
Adabelief optimizer: Adapting stepsizes by the belief in observed gradients
Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha Dvornek, Xenophon Papademetris, and James S Duncan. 2020 · 2010
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Large scale datasets for image and video captioning in italian
Antonio Scaiella, Danilo Croce, and Roberto Basili. 2019 · 2019
Cited alongside, same era.
wav2vec: Unsupervised pre-training for speech recognition
Steffen Schneider, Alexei Baevski, Ronan Collobert, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Cited alongside, same era.
“you sound just like your father” commercial machine translation systems include stylistic biases
Dirk Hovy, Federico Bianchi, and Tommaso Fornaciari. 2020 · 2020
Cited alongside, same era.
Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych. 2020 · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
On the gap between adoption and understanding in NLP
Federico Bianchi and Dirk Hovy. 2021 · 2021
Closest in time.
BERT goes shopping: Comparing distributional models for product representations
Federico Bianchi, Bingqing Yu, and Jacopo Tagliabue. 2021 · 2021
Closest in time.
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, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
Closest in time.
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, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning
Krishna Srinivasan, Karthik Raman, Jiecao Chen, Michael Bendersky, and Marc Najork. 2021 · 2021
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Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Closest in time.