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Large-scale datasets play a vital role in computer vision.
WordNet: An electronic lexical database
George A Miller · 1998
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Support vector machine active learning with applications to text classification
Simon Tong and Daphne Koller · 2001
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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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Query by committee made real
Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby · 2005
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A visual vocabulary for flower classification
M-E Nilsback and Andrew Zisserman · 2006
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
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Yago: A core of semantic knowledge unifying wordnet and wikipedia
MS Fabian, Kasneci Gjergji, WEIKUM Gerhard, et al · 2007
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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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Large-scale privacy protection in google street view
Andrea Frome, German Cheung, Ahmad Abdulkader, Marco Zennaro, Bo Wu, Alessandro Bissacco, Hartwig Adam, Hartmut Neven, and Luc Vincent · 2009
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Multi-class active learning for image classification
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Active learning literature survey
Burr Settles · 2009
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The act partially amending the copyright act (act no.52 of 2021; enacted may, 2021), 2010
2010
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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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Active testing for face detection and localization
Raphael Sznitman and Bruno Jedynak · 2010
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Combining generative and discriminative models for semantic segmentation of ct scans via active learning
Juan Eugenio Iglesias, Ender Konukoglu, Albert Montillo, Zhuowen Tu, and Antonio Criminisi · 2011
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Estimation lemma — Wikipedia, the free encyclopedia, 2010
Estimation lemma · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Weakly supervised structured output learning for semantic segmentation
Alexander Vezhnevets, Vittorio Ferrari, and Joachim M Buhmann · 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
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. 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
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. 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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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 2014
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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A large-scale car dataset for fine-grained categorization and verification
Linjie Yang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2015
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Multi-class active learning by uncertainty sampling with diversity maximization
Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, and Alexander G Hauptmann · 2015
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Authors guild v. google, inc
Victoria Campbell · 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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Learning representations for automatic colorization
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 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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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
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Sun database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Cited alongside, same era.
Duplicate image detection with perceptual hashing in python
Hoyt Ben · 2017
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Cited alongside, same era.
spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
The open images dataset v4
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al · 2020
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Prototypical contrastive learning of unsupervised representations
Junnan Li, Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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Yago 4: A reason-able knowledge base
Thomas Pellissier Tanon, Gerhard Weikum, and Fabian M. Suchanek · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2020
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Rethinking pre-training and self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D Cubuk, and Quoc V Le · 2020
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A new dataset of dog breed images and a benchmark for fine-grained classification
Ding-Nan Zou, Song-Hai Zhang, Tai-Jiang Mu, and Min Zhang · 2020
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Billion-scale pretraining with vision transformers for multi-task visual representations
Josh Beal, Hao-Yu Wu, Dong Huk Park, Andrew Zhai, and Dmitry Kislyuk · 2021
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The iwildcam 2021 competition dataset
Sara Beery, Arushi Agarwal, Elijah Cole, and Vighnesh Birodkar · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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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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Batch active learning at scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, and Sanjiv Kumar · 2021
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Daniel D’souza, Zach Nussbaum, Chirag Agarwal, and Sara Hooker · 2021
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Multi-task self-training for learning general representations
Golnaz Ghiasi, Barret Zoph, Ekin D Cubuk, Quoc V Le, and Tsung-Yi Lin · 2021
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Semi-supervised active learning with temporal output discrepancy
Siyu Huang, Tianyang Wang, Haoyi Xiong, Jun Huan, and Dejing Dou · 2021
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Danish fungi 2020–not just another image recognition dataset
Lukáš Picek, Milan Šulc, Jiří Matas, Jacob Heilmann-Clausen, Thomas S Jeppesen, Thomas Læssøe, and Tobias Frøslev · 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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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs, 2021
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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A study of face obfuscation in imagenet
Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2021
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Re-labeling imagenet: from single to multi-labels, from global to localized labels
Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, and Sanghyuk Chun · 2021
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