Cited by 11Multimodal Machine Learning ApplicationsTopic ModelingDomain Adaptation and Few-Shot Learning

Multi-stage Pre-training over Simplified Multimodal Pre-training Models

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Liu, Tongtong, Feng, Fangxiang, Wang, Xiaojie·2021·Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Abstract

Tongtong Liu, Fangxiang Feng, Xiaojie Wang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

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Crossref · 10.18653/v1/2021.acl-long.199

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  • Child et al. present sparse factorizations of the attention matrix to reduce the quadratical complexity to O(n√n).

    “Child et al. [286] present sparse factorizations of the attention matrix to reduce the quadratical complexity to O(n √ n)”

    From Multimodal learning with transformers: A survey (2023) · cites this paper as [286]

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  1. Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.

Cited by

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Authors
Liu, T., Feng, F., & Wang, X.
Affiliations
Beijing University of Posts and Telecommunications (CN)
Venue
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Published
2021
OpenAlex
W3175836671
Cited by
11
References
33
Citations by year
2025: 3 · 2024: 3 · 2023: 3 · 2022: 2
FWCI
0.65
Type
conference paper
Language
en
Open access
Licence
cc-by
Funders
  • National Natural Science Foundation of China (NSFC62076032)
  • National Key Research and Development Program of China (2020YFF0305302)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
multimodal pre-training, multimodal representation learning, vision-language models

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