Cross Modal Retrieval With Noisy

Cross-modal retrieval with noise-corresponding learning aims to mitigate the negative impact of mismatched pairs (such as false positives and false negatives) in multi-modal data, thereby improving retrieval accuracy and robustness. This task achieves more precise matching and retrieval results in scenarios like image-text cross-modal retrieval by optimizing algorithms to reduce the interference of noisy data during model training.