Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching
Author:
Affiliation:
1. Shandong University, Jinan, China
2. Shandong Jianzhu University, Jinan, China
3. Shandong University, Qingdao, China
4. Harbin Institute of Technology (Shenzhen), Shenzhen, China
Abstract
Funder
National Natural Science Foundation of China
Shandong Provincial Natural Science Foundation
Science and Technology Innovation Program for Distinguished Young Scholars of Shandong Province Higher Education Institutions
Publisher
Association for Computing Machinery (ACM)
Link
https://dl.acm.org/doi/pdf/10.1145/3662732
Reference61 articles.
1. Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor, and Kevin McGuinness. 2019. Unsupervised Label Noise Modeling and Loss Correction. In Proceedings of the International Conference on Machine Learning, Vol. 97. 312–321.
2. Global Relation-Aware Attention Network for Image-Text Retrieval
3. IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
4. Cross-modal Graph Matching Network for Image-text Retrieval
5. Junyoung Chung Caglar Gülcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv:2201.08239. Retrieved from https://arxiv.org/abs/1412.3555
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