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Although an object may appear in numerous contexts, we often describe it in a limited number of ways.
On the limited memory bfgs method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
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Combining labeled and unlabeled data with co-training
Avrim Blum and Tom Mitchell · 1998
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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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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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Automated flower classification over a large number of classes
M-E. Nilsback and A. Zisserman · 2008
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 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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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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SUN database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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An Analysis of Single Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Cats and dogs
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 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
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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A multi-view embedding space for modeling internet images, tags, and their semantics
Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik · 2014
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Referitgame: Referring to objects in photographs of natural scenes
Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier · 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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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Love thy neighbors: Image annotation by exploiting image metadata
Justin Johnson, Lamberto Ballan, and Li Fei-Fei · 2015
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Skip-thought vectors
Ryan Kiros, Yukun Zhu, Russ R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
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CIDEr: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
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Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition
Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, and Yizhou Yu · 2015
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SPICE: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2016
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Are elephants bigger than butterflies? reasoning about sizes of objects
Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
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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
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
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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
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Learning visual n-grams from web data
Ang Li, Allan Jabri, Armand Joulin, and Laurens van der Maaten · 2017
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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An efficient framework for learning sentence representations
Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
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MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou · 2020
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What should not be contrastive in contrastive learning
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell · 2020
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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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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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An empirical study of training self-supervised vision transformers
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Lajanugen Logeswaran and Honglak Lee · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 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
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Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Self-labelling via simultaneous clustering and representation learning
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi · 2019
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
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Xinlei Chen*, Saining Xie*, and Kaiming He · 2021
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Virtex: Learning visual representations from textual annotations
Karan Desai and Justin Johnson · 2021
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RedCaps: Web-curated image-text data created by the people, for the people
Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson · 2021
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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 · 2021
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With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2021
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Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering
Mohamed El Banani, Luya Gao, and Justin Johnson · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
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Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2021
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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Pri3d: Can 3d priors help 2d representation learning?
Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner · 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 Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Object-aware cropping for self-supervised learning
Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, and Dilip Krishnan · 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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Casting your model: Learning to localize improves self-supervised representations
Ramprasaath R Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik · 2021
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Cohesiv: Contrastive object and hand embedding segmentation in video
Dandan Shan, Richard Ely Locke Higgins, and David Fouhey · 2021
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LoFTR: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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Divide and contrast: Self-supervised learning from uncurated data
Yonglong Tian, Olivier J Henaff, and Aäron van den Oord · 2021
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Resnet strikes back: An improved training procedure in timm
Ross Wightman, Hugo Touvron, and Herve Jegou · 2021
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Towers of babel: Combining images, language, and 3d geometry for learning multimodal vision
Xiaoshi Wu, Hadar Averbuch-Elor, Jin Sun, and Noah Snavely · 2021
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Weakly supervised contrastive learning
Mingkai Zheng, Fei Wang, Shan You, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu · 2021
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Democratizing contrastive language-image pre-training: A CLIP benchmark of data, model, and supervision
Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, and Jing Shao · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Uniclip: Unified framework for contrastive language-image pre-training
Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, and Junmo Kim · 2022
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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 · 2022
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Slip: Self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie · 2022
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Normalized contrastive learning for text-video retrieval
Yookoon Park, Mahmoud Azab, Bo Xiong, Seungwhan Moon, Florian Metze, Gourab Kundu, and Kirmani Ahmed · 2022
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Crafting better contrastive views for siamese representation learning
Xiangyu Peng, Kai Wang, Zheng Zhu, Mang Wang, and Yang You · 2022
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Integrating Language Guidance into Vision-based Deep Metric Learning
Karsten Roth, Oriol Vinyals, and Zeynep Akata · 2022
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Learning viewpoint-agnostic visual representations by recovering tokens in 3d space
Jinghuan Shang, Srijan Das, and Michael S Ryoo · 2022
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Groupvit: Semantic segmentation emerges from text supervision
Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang · 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
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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The hidden uniform cluster prior in self-supervised learning
Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas · 2023
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