Zeyuan Gu

Noisy Correspondence Learning with Modality Gap Direction Correction

Cross-modal retrieval is crucial for discovering latent correspondences across different modalities. However, existing methods typically assume that training data are well-aligned, an unrealistic assumption since real-world datasets inevitably contain noisy correspondences. Many current approaches a