CVPR 2023 - Noisy Correspondence Learning with Meta Similarity Correction

In this episode we discuss Noisy Correspondence Learning with Meta Similarity Correction by Haochen Han, Kaiyao Miao, Qinghua Zheng, Minnan Luo. The paper proposes a Meta Similarity Correction Network (MSCN) to address the problem of noisy correspondence datasets, which causes performance degradation in cross-modal retrieval methods. MSCN provides reliable similarity scores by viewing a binary classification task as the meta-process that encourages discrimination from positive and negative meta-data. Additionally, the paper presents an effective data purification strategy that uses meta-data as prior knowledge to remove noisy samples. The proposed method is evaluated in both synthetic and real-world noise datasets, demonstrating its effectiveness in improving cross-modal retrieval performance.