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.
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