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Adapters present a promising solution to the catastrophic forgetting problem in continual learning.
Catastrophic forgetting in connectionist networks
Robert M French · 1999
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A multi-world approach to question answering about real-world scenes based on uncertain input
Mateusz Malinowski and Mario Fritz · 2014
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VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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Visual7W: Grounded Question Answering in Images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei · 2016
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi · 2017
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Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2018
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VizWiz Grand Challenge: Answering visual questions from blind people
Danna Gurari, Qing Li, Abigale J Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P Bigham · 2018
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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
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Efficient lifelong learning with A-GEM
Arslan Chaudhry, Marc’Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny · 2019
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Psycholinguistics meets continual learning: Measuring catastrophic forgetting in visual question answering
Claudio Greco, Barbara Plank, Raquel Fernández, and Raffaella Bernardi · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland and Iain Murray · 2019
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ViLT: Vision-and-language transformer without convolution or region supervision
Wonjae Kim, Bokyung Son, and Ildoo Kim · 2021
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AdapterFusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 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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Memory efficient continual learning with transformers
Beyza Ermis, Giovanni Zappella, Martin Wistuba, Aditya Rawal, and Cedric Archambeau · 2022
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Symbolic Replay: Scene graph as prompt for continual learning on vqa task
Stan Weixian Lei, Difei Gao, Jay Zhangjie Wu, Yuxuan Wang, Wei Liu, Mengmi Zhang, and Mike Zheng Shou · 2022
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Intermediate-task transfer learning with pretrained language models: When and why does it work?
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Continual sequence generation with adaptive compositional modules
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CL-CrossVQA: A continual learning benchmark for cross-domain visual question answering
Yao Zhang, Haokun Chen, Ahmed Frikha, Yezi Yang, Denis Krompass, Gengyuan Zhang, Jindong Gu, and Volker Tresp
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CLiMB: A continual learning benchmark for vision-and-language tasks
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Continual learning for instruction following from realtime feedback
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VL-Adapter: Parameter-efficient transfer learning for vision-and-language tasks
Yi-Lin Sung, Jaemin Cho, and Mohit Bansal · 2022
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