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Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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VisualBERT: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019 · 1908
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Distinguishing deceptive from non-deceptive speech
Julia Hirschberg, Stefan Benus, Jason Brenier, Frank Enos, Sarah Hoffman, Sarah Gilman, Cynthia Girand, Martin Graciarena, Andreas Kathol, Laura Michaelis, Bryan Pellom, Elizabeth Shriberg, and Andreas Stolcke. 2005 · 2005
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Evaluation measures for ordinal regression
Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2009 · 2009
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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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A multimodal framework for the detection of hateful memes
Phillip Lippe, Nithin Holla, Shantanu Chandra, Santhosh Rajamanickam, Georgios Antoniou, Ekaterina Shutova, and Helen Yannakoudakis. 2020 · 2012
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Vilio: State-of-the-art visio-linguistic models applied to hateful memes
Niklas Muennighoff. 2020 · 2012
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Detecting hateful memes using a multimodal deep ensemble
Vlad Sandulescu. 2020 · 2012
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Riza Velioglu and Jewgeni Rose. 2020 · 2012
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DeViSE: A deep visual-semantic embedding model
Andrea Frome, Greg S. Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov. 2013 · 2013
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2015 · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman. 2015 · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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“Why should i trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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What value do explicit high level concepts have in vision to language problems?
Qi Wu, Chunhua Shen, Lingqiao Liu, Anthony Dick, and Anton Van Den Hengel. 2016 · 2016
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Inter-annotator agreement in sentiment analysis: Machine learning perspective
Victoria Bobicev and Marina Sokolova. 2017 · 2017
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Deep learning driven multimodal fusion for automated deception detection
Mandar Gogate, Ahsan Adeel, and Amir Hussain. 2017 · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017 · 2017
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Hybrid attention based multimodal network for spoken language classification
Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018 · 2018
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The multimodal construction of race: a review of critical race theory research
Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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LXMERT: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 2019
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What was written vs. who read it: News media profiling using text analysis and social media context
Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, and Preslav Nakov. 2020 · 2020
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UNITER: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020 · 2020
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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
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Kathy A. Mills and Len Unsworth. 2018 · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. 2018 · 2018
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Multimodal approach for multimedia injurious contents blocking
Byeongtae Ahn and Seok-Woo Jang. 2019 · 2019
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Michael A Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen. 2019 · 2019
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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 · 2019
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Multi-modal sarcasm detection in Twitter with hierarchical fusion model
Yitao Cai, Huiyu Cai, and Xiaojun Wan. 2019 · 2019
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Racial bias in hate speech and abusive language detection datasets
Thomas Davidson, Debasmita Bhattacharya, and Ingmar Weber. 2019 · 2019
Cited alongside, same era.
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2020 · 2020
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Hate speech detection and racial bias mitigation in social media based on BERT model
Marzieh Mozafari, Reza Farahbakhsh, and Noël Crespi. 2020 · 2020
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SemEval-2020 task 8: Memotion analysis- the visuo-lingual metaphor!
Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, Viswanath Pulabaigari, and Björn Gambäck. 2020 · 2020
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Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text
Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, and Paul Buitelaar. 2020 · 2020
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Demoting racial bias in hate speech detection
Mengzhou Xia, Anjalie Field, and Yulia Tsvetkov. 2020 · 2020
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Unified vision-language pre-training for image captioning and VQA
Luowei Zhou, Hamid Palangi, Lei Zhang, Houdong Hu, Jason Corso, and Jianfeng Gao. 2020 · 2020
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“Subverting the Jewtocracy”: Online antisemitism detection using multimodal deep learning
Mohit Chandra, Dheeraj Pailla, Himanshu Bhatia, Aadilmehdi Sanchawala, Manish Gupta, Manish Shrivastava, and Ponnurangam Kumaraguru. 2021 · 2021
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SemEval-2021 Task 6: Detection of persuasion techniques in texts and images
Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, and Giovanni Da San Martino. 2021b · 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 · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021 · 2021
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Findings of the shared task on troll meme classification in Tamil
Shardul Suryawanshi and Bharathi Raja Chakravarthi. 2021 · 2021
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Detecting medical misinformation on social media using multimodal deep learning
Zuhui Wang, Zhaozheng Yin, and Young Anna Argyris. 2021 · 2021
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Multimodal learning for hateful memes detection
Yi Zhou, Zhenhao Chen, and Huiyuan Yang. 2021b · 2021
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