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Continual Learning (CL) often relies on the availability of extensive annotated datasets, an assumption that is unrealistically time-consuming and costly in practice.
Learning object categories from google’s image search
Robert Fergus, Li Fei-Fei, Pietro Perona, and Andrew Zisserman · 2005
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Keywords to visual categories: Multiple-instance learning forweakly supervised object categorization
Sudheendra Vijayanarasimhan and Kristen Grauman · 2008
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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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Optimol: automatic online picture collection via incremental model learning
Li-Jia Li and Li Fei-Fei · 2010
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Harvesting image databases from the web
Florian Schroff, Antonio Criminisi, and Andrew Zisserman · 2010
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Neil: Extracting visual knowledge from web data
Xinlei Chen, Abhinav Shrivastava, and Abhinav Gupta · 2013
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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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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Birdsnap: Large-scale fine-grained visual categorization of birds
Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L Alexander, David W Jacobs, and Peter N Belhumeur · 2014
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Learning everything about anything: Webly-supervised visual concept learning
Santosh K Divvala, Ali Farhadi, and Carlos Guestrin · 2014
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Webly supervised learning of convolutional networks
Xinlei Chen and Abhinav Gupta · 2015
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Learning visual features from large weakly supervised data
Armand Joulin, Laurens Van Der Maaten, Allan Jabri, and Nicolas Vasilache · 2016
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The unreasonable effectiveness of noisy data for fine-grained recognition
Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, and Li Fei-Fei · 2016
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Webly supervised semantic segmentation
Bin Jin, Maria V Ortiz Segovia, and Sabine Susstrunk · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten · 2018
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Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Yu A Malkov and Dmitry A Yashunin · 2018
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Bootstrapping the performance of webly supervised semantic segmentation
Tong Shen, Guosheng Lin, Chunhua Shen, and Ian Reid · 2018
Cited alongside, same era.
Selection via proxy: Efficient data selection for deep learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia · 2019
Cited alongside, same era.
Large-scale weakly-supervised pre-training for video action recognition
Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan · 2019
Cited alongside, same era.
Sampling bias in deep active classification: An empirical study
Ameya Prabhu, Charles Dognin, and Maneesh Singh · 2019
Cited alongside, same era.
Mining cross-image semantics for weakly supervised semantic segmentation
Guolei Sun, Wenguan Wang, Jifeng Dai, and Luc Van Gool · 2020
Cited alongside, same era.
Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2022
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Clactive: Episodic memories for rapid active learning
Sri Aurobindo Munagala, Sidhant Subramanian, Shyamgopal Karthik, Ameya Prabhu, and Anoop Namboodiri · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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
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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
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Webly supervised knowledge embedding model for visual reasoning
Wenbo Zheng, Lan Yan, Chao Gou, and Fei-Yue Wang · 2020
Cited alongside, same era.
Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
Cited alongside, same era.
An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
Cited alongside, same era.
Self-supervised training enhances online continual learning
Jhair Gallardo, Tyler L Hayes, and Christopher Kanan · 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 Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
Cited alongside, same era.
Learning from long-tailed data with noisy labels
Shyamgopal Karthik, Jérome Revaud, and Boris Chidlovskii · 2021
Cited alongside, same era.
The clear benchmark: Continual learning on real-world imagery
Zhiqiu Lin, Jia Shi, Deepak Pathak, and Deva Ramanan · 2021
Cited alongside, same era.
Later among the works it cites.
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, et al · 2022
Later among the works it cites.
A data augmentation perspective on diffusion models and retrieval
Max F Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell · 2023
Closest in time.
Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev · 2023
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Calip: Zero-shot enhancement of clip with parameter-free attention
Ziyu Guo, Renrui Zhang, Longtian Qiu, Xianzheng Ma, Xupeng Miao, Xuming He, and Bin Cui · 2023
Closest in time.
Rapid adaptation in online continual learning: Are we evaluating it right?
Hasan Abed Al Kader Hammoud, Ameya Prabhu, Ser-Nam Lim, Philip HS Torr, Adel Bibi, and Bernard Ghanem · 2023
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A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Martin Mundt, Yongwon Hong, Iuliia Pliushch, and Visvanathan Ramesh · 2023
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OpenAI · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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What does a platypus look like? generating customized prompts for zero-shot image classification
Sarah Pratt, Ian Covert, Rosanne Liu, and Ali Farhadi · 2023
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Waffling around for performance: Visual classification with random words and broad concepts
Karsten Roth, Jae Myung Kim, A. Sophia Koepke, Oriol Vinyals, Cordelia Schmid, and Zeynep Akata · 2023
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Agile modeling: From concept to classifier in minutes
Otilia Stretcu, Edward Vendrow, Kenji Hata, Krishnamurthy Viswanathan, Vittorio Ferrari, Sasan Tavakkol, Wenlei Zhou, Aditya Avinash, Emming Luo, Neil Gordon Alldrin, et al · 2023
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Sus-x: Training-free name-only transfer of vision-language models
Vishaal Udandarao, Ankush Gupta, and Samuel Albanie · 2023
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Neural priming for sample-efficient adaptation
Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, and Ali Farhadi · 2023
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Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li · 2023
Closest in time.
Yifei Zhou, Juntao Ren, Fengyu Li, Ramin Zabih, and Ser-Nam Lim · 2023
Closest in time.