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While vision-and-language models significantly advance in many fields, the challenge of continual learning is unsolved.
Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory
James L McClelland, Bruce L McNaughton, and Randall C O’Reilly · 1995
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 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, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, et al · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2018
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
Cited alongside, same era.
Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, et al · 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
Cited alongside, same era.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, et al · 2019
Cited alongside, same era.
Visual entailment: A novel task for fine-grained image understanding
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav · 2019
Cited alongside, same era.
From recognition to cognition: Visual commonsense reasoning
Multimodal continual learning using online dictionary updating
Fuchun Sun, Huaping Liu, Chao Yang, and Bin Fang · 2020
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Multi-modal meta continual learning
Sibo Gai, Zhengyu Chen, and Donglin Wang · 2021
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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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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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Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems
Laura Von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Julius Pfrommer, Annika Pick, Rajkumar Ramamurthy, et al · 2021
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Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
Cited alongside, same era.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
Cited alongside, same era.
Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, et al · 2020
Cited alongside, same era.
Semantic-aware scene recognition
Alejandro López-Cifuentes, Marcos Escudero-Vinolo, Jesús Bescós, and Álvaro García-Martín · 2020
Cited alongside, same era.
Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
Cited alongside, same era.
Triple-memory networks: A brain-inspired method for continual learning
Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu, and Yi Zhong
Cited in the paper.
Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning
Liyuan Wang, Kuo Yang, Chongxuan Li, Lanqing Hong, Zhenguo Li, and Jun Zhu
Cited in the paper.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning
Victor Weixin Liang, Yuhui Zhang, Yongchan Kwon, Serena Yeung, and James Y Zou · 2022
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Climb: A continual learning benchmark for vision-and-language tasks
Tejas Srinivasan, Ting-Yun Chang, Leticia Pinto Alva, Georgios Chochlakis, Mohammad Rostami, and Jesse Thomason · 2022
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Continual object detection via prototypical task correlation guided gating mechanism
Binbin Yang, Xinchi Deng, Han Shi, Changlin Li, Gengwei Zhang, Hang Xu, Shen Zhao, Liang Lin, and Xiaodan Liang · 2022
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Coda-prompt: Continual decomposed attention-based prompting for rehearsal-free continual learning
James Seale Smith, Leonid Karlinsky, Vyshnavi Gutta, Paola Cascante-Bonilla, Donghyun Kim, Assaf Arbelle, Rameswar Panda, Rogerio Feris, and Zsolt Kira · 2023
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