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Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works.
WordNet: An electronic lexical database
George A Miller · 1998
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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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The pascal visual object classes (VOC) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Computer vision: algorithms and applications
Richard Szeliski · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Evaluating knowledge transfer and zero-shot learning in a large-scale setting
Marcus Rohrbach, Michael Stark, and Bernt Schiele · 2011
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The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
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The Caltech-UCSD birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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The MNIST database of handwritten digit images for machine learning research
Li Deng · 2012
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Wiktionary: A new rival for expert-built lexicons? Exploring the possibilities of collaborative lexicography
Christian M Meyer and Iryna Gurevych · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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SUN attribute database: Discovering, annotating, and recognizing scene attributes
Genevieve Patterson and James Hays · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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https://www.kaggle.com/
FER 2013: Kaggle challenges in representation learning facial expression recognition · 2013
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A new performance measure and evaluation benchmark for road detection algorithms
Jannik Fritsch, Tobias Kuehnl, and Andreas Geiger · 2013
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, and Holger Winnemoeller · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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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
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions
Sven Bambach, Stefan Lee, David Crandall, and Chen Yu · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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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
Cited alongside, same era.
Semi-supervised vocabulary-informed learning
Yanwei Fu and Leonid Sigal · 2016
Cited alongside, same era.
YFCC100M: The new data in multimedia research
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
Cited alongside, same era.
Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
Cited alongside, same era.
Learning visual N-grams from web data
Ang Li, Allan Jabri, Armand Joulin, and Laurens Van Der Maaten · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
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
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An empirical study of training self-supervised visual transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Dynamic head: Unifying object detection heads with attentions
Xiyang Dai, Yinpeng Chen, Bin Xiao, Dongdong Chen, Mengchen Liu, Lu Yuan, and Lei Zhang · 2021
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CLIP-adapter: Better vision-language models with feature adapters
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao · 2021
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Zero-shot detection via vision and language knowledge distillation
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui · 2021
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
Reinforced temporal attention and split-rate transfer for depth-based person re-identification
Nikolaos Karianakis, Zicheng Liu, Yinpeng Chen, and Stefano Soatto · 2018
Cited alongside, same era.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
Cited alongside, same era.
Rotation equivariant CNNs for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Cited alongside, same era.
Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
Cited alongside, same era.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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OpenCLIP, July 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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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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Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath R Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi · 2021
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Value: A multi-task benchmark for video-and-language understanding evaluation
Linjie Li, Jie Lei, Zhe Gan, Licheng Yu, Yen-Chun Chen, Rohit Pillai, Yu Cheng, Luowei Zhou, Xin Eric Wang, William Yang Wang, et al · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm
Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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SLIP: Self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie · 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
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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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 · 2021
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CPT: Colorful prompt tuning for pre-trained vision-language models
Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun · 2021
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Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
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Open-vocabulary object detection using captions
Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang · 2021
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Iglue: A benchmark for transfer learning across modalities, tasks, and languages
Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer, Siva Reddy, Desmond Elliott, Edoardo Maria Ponti, and Ivan Vulić · 2022
Closest in time.
Context autoencoder for self-supervised representation learning
Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang · 2022
Closest in time.
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Grounded language-image pre-training
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al · 2022
Closest in time.
K-LITE: Learning transferable visual models with external knowledge
Sheng Shen, Chunyuan Li, Xiaowei Hu, Yujia Xie, Jianwei Yang, Pengchuan Zhang, Anna Rohrbach, Zhe Gan, Lijuan Wang, Lu Yuan, Ce Liu, Kurt Keutzer, Trevor Darrell, and Jianfeng Gao · 2022
Closest in time.
VL-adapter: Parameter-efficient transfer learning for vision-and-language tasks
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
Closest in time.
UniCL: unified contrastive learning in image-text-label space
Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Bin Xiao, Lu Yuan, Ce Liu, and Jianfeng Gao · 2022
Closest in time.
RegionCLIP: Region-based language-image pretraining
Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al · 2022
Closest in time.
Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Closest in time.
Vlue: A multi-task multi-dimension benchmark for evaluating vision-language pre-training
Wangchunshu Zhou, Yan Zeng, Shizhe Diao, and Xinsong Zhang · 2022
Closest in time.