Fetching the paper…
Reading the bibliography…
Large web-sourced multimodal datasets have powered a slew of new methods for learning general-purpose visual representations, advancing the state of the art in computer vision and revolutionizing zero- and few-shot recognition.
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
Earlier work this paper cites.
Microsoft coco captions: Data collection and evaluation server, 2015
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C. Lawrence Zitnick · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge, 2015
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
Earlier work this paper cites.
Deep learning scaling is predictable, empirically, 2017
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
Earlier work this paper cites.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
Earlier work this paper cites.
Distribution density, tails, and outliers in machine learning: Metrics and applications
Nicholas Carlini, Úlfar Erlingsson, and Nicolas Papernot · 2019
Earlier work this paper cites.
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
Earlier work this paper cites.
Do deep neural networks learn shallow learnable examples first?
Karttikeya Mangalam and Vinay Uday Prabhu · 2019
Earlier work this paper cites.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Earlier work this paper cites.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Earlier work this paper cites.
Satrajit Chatterjee · 2020
Earlier work this paper cites.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
Earlier work this paper cites.
What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Characterizing structural regularities of labeled data in overparameterized models
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, and Michael C Mozer · 2020
Earlier work this paper cites.
Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Earlier work this paper cites.
Oscar: Object-semantics aligned pre-training for vision-language tasks
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al · 2020
Earlier work this paper cites.
The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli · 2020
Cited alongside, same era.
A large-scale study of representation learning with the visual task adaptation benchmark, 2020
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, and Neil Houlsby · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Fast: Faster arbitrarily-shaped text detector with minimalist kernel representation, 2021
Zhe Chen, Jiahao Wang, Wenhai Wang, Guo Chen, Enze Xie, Ping Luo, and Tong Lu · 2021
Cited alongside, same era.
Scaling laws for transfer, 2021
Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish · 2021
Unified scaling laws for routed language models, 2022
Aidan Clark, Diego de las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew Johnson, Katie Millican, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Jack Rae, Erich Elsen, Koray Kavukcuoglu, and Karen Simonyan · 2022
Later among the works it cites.
Data determines distributional robustness in contrastive language image pre-training (clip)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, and Ludwig Schmidt · 2022
Later among the works it cites.
Finetune like you pretrain: Improved finetuning of zero-shot vision models, 2022
Sachin Goyal, Ananya Kumar, Sankalp Garg, Zico Kolter, and Aditi Raghunathan · 2022
Later among the works it cites.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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
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.
Align before fuse: Vision and language representation learning with momentum distillation
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi · 2021
Cited alongside, same era.
Slip: Self-supervision meets language-image pre-training, 2021
Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie · 2021
Cited alongside, same era.
Combined scaling for zero-shot transfer learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, et al · 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
Cited alongside, same era.
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
Cited alongside, same era.
Gal Kaplun, Nikhil Ghosh, Saurabh Garg, Boaz Barak, and Preetum Nakkiran · 2022
Later among the works it cites.
Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm, 2022
Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan · 2022
Later among the works it cites.
Characterizing datapoints via second-split forgetting
Pratyush Maini, Saurabh Garg, Zachary Chase Lipton, and J Zico Kolter · 2022
Later among the works it cites.
Prioritized training on points that are learnable, worth learning, and not yet learnt
Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, et al · 2022
Later among the works it cites.
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
Later among the works it cites.
Scaling vision transformers, 2022
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
Later among the works it cites.
Semdedup: Data-efficient learning at web-scale through semantic deduplication
Amro Abbas, Kushal Tirumala, Dániel Simig, Surya Ganguli, and Ari S Morcos · 2023
Closest in time.
Datacomp: In search of the next generation of multimodal datasets, 2023
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, and Ludwig Schmidt · 2023
Closest in time.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 2023
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Closest in time.
Improving multimodal datasets with image captioning, 2023
Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, and Ludwig Schmidt · 2023
Closest in time.
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
Closest in time.
Beyond neural scaling laws: beating power law scaling via data pruning, 2023
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S. Morcos · 2023
Closest in time.
Cit: Curation in training for effective vision-language data
Hu Xu, Saining Xie, Po-Yao Huang, Licheng Yu, Russell Howes, Luke Zettlemoyer Gargi Ghosh, and Christoph Feichtenhofer · 2023
Closest in time.