Fetching the paper…
Reading the bibliography…
Continual Visual Question Answering (CVQA) based on pre-trained models(PTMs) has achieved promising progress by leveraging prompt tuning to enable continual multi-modal learning.
A simple weight decay can improve generalization
Anders Krogh and John Hertz · 1991
Earlier work this paper cites.
A lifelong learning perspective for mobile robot control
Sebastian Thrun · 1995
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
VQA: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Overcoming language priors in visual question answering with adversarial regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee · 2018
Earlier work this paper cites.
Can we gain more from orthogonality regularizations in training deep networks?
Nitin Bansal, Xiaohan Chen, and Zhangyang Wang · 2018
Earlier work this paper cites.
Bottom -
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
Earlier work this paper cites.
Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting
Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, and Caiming Xiong · 2019
Earlier work this paper cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Earlier work this paper cites.
Hao Tan and Mohit Bansal · 2019
Earlier work this paper cites.
Aligning visual regions and textual concepts for semantic-grounded image representations
Fenglin Liu, Yuanxin Liu, Xuancheng Ren, Xiaodong He, and Xu Sun · 2019
Earlier work this paper cites.
Dark experience for general continual learning: A strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text -
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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.
Multi-domain multi-task rehearsal for lifelong learning
Fan Lyu, Shuai Wang, Wei Feng, Zihan Ye, Fuyuan Hu, and Song Wang · 2021
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
Cited alongside, same era.
A survey on masked autoencoder for self -
Chaoning Zhang, Chenshuang Zhang, Junha Song, John Seon Keun Yi, Kang Zhang, and In So Kweon · 2022
Later among the works it cites.
Vqacl: A novel visual question answering continual learning setting
Xi Zhang, Feifei Zhang, and Changsheng Xu · 2023
Later among the works it cites.
Decouple before interact: Multi -
Zi Qian, Xin Wang, Xuguang Duan, Pengda Qin, Yuhong Li, and Wenwu Zhu · 2023
Later among the works it cites.
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 · 2023
Later among the works it cites.
Measuring asymmetric gradient discrepancy in parallel continual learning
Fan Lyu, Qing Sun, Fanhua Shang, Liang Wan, and Wei Feng · 2023
Later among the works it cites.
MAPLE: Multi -
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Attention bottlenecks for multimodal fusion
Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, and Chen Sun · 2021
Cited alongside, same era.
Exploring example influence in continual learning
Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, and Liang Wan · 2022
Cited alongside, same era.
Balanced multimodal learning via on-the-fly gradient modulation
Xiaokang Peng, Yake Wei, Andong Deng, Dong Wang, and Di Hu · 2022
Cited alongside, same era.
DyTox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2022
Cited alongside, same era.
Difnet: Boosting visual information flow for image captioning
Mingrui Wu, Xuying Zhang, Xiaoshuai Sun, Yiyi Zhou, Chao Chen, Jiaxin Gu, Xing Sun, and Rongrong Ji · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, and Fahad Shahbaz Khan · 2023
Later among the works it cites.
Preventing zero -
Zangwei Zheng, Mingyuan Ma, Kai Wang, Ziheng Qin, Xiangyu Yue, and Yang You · 2023
Later among the works it cites.
Enhancing continual learning in visual question answering with modality -
Malvina Nikandrou, Georgios Pantazopoulos, Ioannis Konstas, and Alessandro Suglia · 2024
Later among the works it cites.
Yuliang Cai and Mohammad Rostami · 2024
Later among the works it cites.
Semantic residual prompts for continual learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini, Enver Sangineto, Lorenzo Bonicelli, Angelo Porrello, and Simone Calderara · 2024
Later among the works it cites.
Understanding driving risks via prompt learning
Yubo Chang, Fan Lyu, Zhang Zhang, and Liang Wang · 2024
Later among the works it cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2024
Later among the works it cites.