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Contrastive Language-Image Pre-training (CLIP) has become the standard for cross-modal image-text representation learning.
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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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Fergus Rob, and Pietro Perona. 2004 · 2004
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Automated flower classification over a large number of classes
M-E. Nilsback and A. Zisserman. 2008 · 2008
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Similarity-based classification: Concepts and algorithms
Yihua Chen, Eric K Garcia, Maya R Gupta, Ali Rahimi, and Luca Cazzanti. 2009 · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li. 2009 · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al. 2009 · 2009
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SUN database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. 2010 · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011 · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. 2011 · 2011
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 2013 · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi. 2013 · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. 2014 · 2014
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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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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. 2015 · 2015
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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 · 2018
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab, and Ari S. Morcos. 2020 · 2020
Cited alongside, same era.
Hard negative mixing for contrastive learning
Yannis Kalantidis, Mert Bülent Sariyildiz, Noé Pion, Philippe Weinzaepfel, and Diane Larlus. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola. 2020 · 2020
Cited alongside, same era.
Devlbert: Learning deconfounded visio-linguistic representations
Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, and Fei Wu. 2020 · 2020
Cited alongside, same era.
Improving cross-modal understanding in visual dialog via contrastive learning
Feilong Chen, Xiuyi Chen, Shuang Xu, and Bo Xu. 2022 · 2022
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Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, and Jing Shao. 2022 · 2022
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Cyclip: Cyclic contrastive language-image pretraining
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A Rossi, Vishwa Vinay, and Aditya Grover. 2022 · 2022
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2022 · 2022
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Boosting contrastive self-supervised learning with false negative cancellation
Tri Huynh, Simon Kornblith, Matthew R. Walter, Michael Maire, and Maryam Khademi. 2022 · 2022
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When is memorization of irrelevant training data necessary for high-accuracy learning?
Gavin Brown, Mark Bun, Vitaly Feldman, Adam Smith, and Kunal Talwar. 2021 · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021 · 2021
Cited alongside, same era.
Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. 2021 · 2021
Cited alongside, same era.
CLOOB: modern hopfield networks with infoloob outperform CLIP
Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David P. Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, and Sepp Hochreiter. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven Chu-Hong Hoi. 2021 · 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, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
BLIP: bootstrapping language-image pre-training for unified vision-language understanding and generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven C. H. Hoi. 2022a · 2022
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SLIP: self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David A. Wagner, and Saining Xie. 2022 · 2022
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Asif: Coupled data turns unimodal models to multimodal without training
Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. 2022 · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. 2022 · 2022
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Max-margin contrastive learning
Anshul Shah, Suvrit Sra, Rama Chellappa, and Anoop Cherian. 2022 · 2022
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Revisiting weakly supervised pre-training of visual perception models
Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross Girshick, Piotr Dollár, and Laurens Van Der Maaten. 2022 · 2022
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Data efficient language-supervised zero-shot recognition with optimal transport distillation
Bichen Wu, Ruizhe Cheng, Peizhao Zhang, Tianren Gao, Joseph E. Gonzalez, and Peter Vajda. 2022 · 2022
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FILIP: fine-grained interactive language-image pre-training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. 2022 · 2022
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Foundational models defining a new era in vision: A survey and outlook
Muhammad Awais, Muzammal Naseer, Salman Khan, Rao Muhammad Anwer, Hisham Cholakkal, Mubarak Shah, Ming-Hsuan Yang, and Fahad Shahbaz Khan. 2023 · 2023
Closest in time.
Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et al. 2023 · 2023
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Filtering, distillation, and hard negatives for vision-language pre-training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, and Dhruv Mahajan. 2023 · 2023
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