[47] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S Kankanhalli. Learning to learn from noisy labeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5051-5059, 2019.
[48] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. Advances in Neural Information Processing Systems, 30, 2017.
[49] Jiangfan Han, Ping Luo, and Xiaogang Wang. Deep self-learning from noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5138-5147, 2019.
[50] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9729-9738, 2020.
[51] Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia. Iterative learning with open-set noisy labels. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pages 8688-8696, 2018.
[52] Jiabo Huang, Qi Dong, Shaogang Gong, and Xiatian Zhu. Unsupervised deep learning by neighbourhood discovery. In International Conference on Machine Learning, pages 2849-2858. PMLR, 2019.
[53] Dara Bahri, Heinrich Jiang, and Maya Gupta. Deep k-nn for noisy labels. In International Conference on Machine Learning, pages 540–550. PMLR, 2020.
[54] Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, and Chao Chen. A topological filter for learning with label noise. Advances in Neural Information Processing Systems, 33:21382-21393, 2020.
[55] Jichang Li, Guanbin Li, Feng Liu, and Yizhou Yu. Neighborhood collective estimation for noisy label identification and correction. In European Conference on Computer Vision, pages 128-145. Springer, 2022.
[56] Diego Ortego, Eric Arazo, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Multi-objective interpolation training for robustness to label noise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6606-6615, 2021.
[57] Junnan Li, Caiming Xiong, and Steven CH Hoi. Learning from noisy data with robust representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9485-9494, 2021.
[58] Ahmet Iscen, Jack Valmadre, Anurag Arnab, and Cordelia Schmid. Learning with neighbor consistency for noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4672-4681, 2022.
[59] Raul Gomez, Lluis Gomez, Jaume Gibert, and Dimosthenis Karatzas. Learning to learn from web data through deep semantic embeddings. In European Conference on Computer Vision Workshops, pages 514-529, 2018.
[60] Peng Hu, Xi Peng, Hongyuan Zhu, Liangli Zhen, and Jie Lin. Learning cross-modal retrieval with noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5403-5413, 2021.
[61] Peng Hu, Zhenyu Huang, Dezhong Peng, Xu Wang, and Xi Peng. Cross-modal retrieval with partially mismatched pairs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[62] Yang Qin, Dezhong Peng, Xi Peng, Xu Wang, and Peng Hu. Deep evidential learning with noisy correspondence for cross-modal retrieval. In Proceedings of the 30th ACM International Conference on Multimedia, pages 4948-4956, 2022.
[63] Huaiwen Zhang, Yang Yang, Fan Qi, Shengsheng Qian, and Changsheng Xu. Robust video-text retrieval via noisy pair calibration. IEEE Transactions on Multimedia, 2023.