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This work introduces CAPIVARA, a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages.
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 · 2004
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman. 2008 · 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 · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton. 2009 · 2009
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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, and Sylvain Gelly. 2020 · 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 · 2010
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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 · 2011
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The MNIST database of handwritten digit images for machine learning research [best of the web]
Li Deng. 2012 · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. 2012 · 2012
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A new performance measure and evaluation benchmark for road detection algorithms
Jannik Fritsch, Tobias Kuehnl, and Andreas Geiger. 2013 · 2013
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Challenges in representation learning: A report on three machine learning contests
Ian J Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, and Dong-Hyun Lee. 2013 · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 2013 · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. 2013 · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. 2014 · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. 2014 · 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 · 2014
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Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016 · 2016
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Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu. 2017 · 2017
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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. 2017 · 2017
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Marian: Fast neural machine translation in C++
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, and Alexandra Birch. 2018 · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 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 · 2018
Cited alongside, same era.
Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. 2018 · 2018
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Jue Wang, Haofan Wang, Jincan Deng, Weijia Wu, and Debing Zhang. 2021 · 2021
Later among the works it cites.
Cross-lingual and multilingual clip
Fredrik Carlsson, Philipp Eisen, Faton Rekathati, and Magnus Sahlgren. 2022 · 2022
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#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
Gabriel Oliveira dos Santos, Esther Luna Colombini, and Sandra Avila. 2022 · 2022
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Hiclip: Contrastive language-image pretraining with hierarchy-aware attention
Shijie Geng, Jianbo Yuan, Yu Tian, Yuxiao Chen, and Yongfeng Zhang. 2022 · 2022
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Large-scale bilingual language-image contrastive learning
Byungsoo Ko and Geonmo Gu. 2022 · 2022
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Cited alongside, same era.
“you sound just like your father” commercial machine translation systems include stylistic biases
Dirk Hovy, Federico Bianchi, and Tommaso Fornaciari. 2020 · 2020
Cited alongside, same era.
The hateful memes challenge: Detecting hate speech in multimodal memes
Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020 · 2020
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Contrastive language-image pre-training for the italian language
Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, and Sri Lakshmi. 2021 · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Slip: Self-supervision meets language-image pre-training
Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie. 2022 · 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, and Mitchell Wortsman. 2022 · 2022
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Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. 2022 · 2022
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AltCLIP: Altering the language encoder in CLIP for extended language capabilities
Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Qinghong Yang, and Ledell Wu. 2023 · 2023
Closest in time.
mlco2/codecarbon: v2.3.0
Benoit Courty, Victor Schmidt, Goyal-Kamal, MarionCoutarel, Boris Feld, Jérémy Lecourt, SabAmine, Kngoyal, Mathilde Léval, Alexis Cruveiller, Ouminasara, Franklin Zhao, Aditya Joshi, Alexis Bogroff, Inimaz, Amine Saboni, Hugues De Lavoreille, Niko Laskaris, Edoardo Abati, LiamConnell, Douglas Blank, Ziyao Wang, Armin Catovic, Michał Stęchły, Alencon, JPW, MinervaBooks, SangamSwadiK, Christian Bauer, and M. Hervé. 2023 · 2023
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Improving clip training with language rewrites
Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, and Yonglong Tian. 2023 · 2023
Closest in time.
Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, and Jieyu Zhang. 2023 · 2023
Closest in time.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023 · 2023
Closest in time.
Parameter-efficient fine-tuning without introducing new latency
Baohao Liao, Yan Meng, and Christof Monz. 2023 · 2023
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The AI Index 2023 Annual Report
Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark, and Raymond Perrault. 2023 · 2023
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Improving multimodal datasets with image captioning
Thao Nguyen, Samir Yitzhak Gadre, Gabriel Ilharco, Sewoong Oh, and Ludwig Schmidt. 2023 · 2023
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Is a caption worth a thousand images? a study on representation learning
Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
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LVLM-eHub: A comprehensive evaluation benchmark for large vision-language models
Peng Xu, Wenqi Shao, Kaipeng Zhang, Peng Gao, Shuo Liu, Meng Lei, Fanqing Meng, Siyuan Huang, Yu Qiao, and Ping Luo. 2023 · 2023
Closest in time.