Meta-Guided Adaptive Weight Learner for Noisy Correspondence

Cross-modal retrieval with noisy correspondences is a critical challenge, especially when data annotations for large-scale multimodal datasets are prone to systematic corruption. To mitigate the impact of noise, many existing methods rely on small-loss sample selection to filter out clean samples. However, these methods can ineluctably result in the inclusion of false positives, which significantly degrade the performance. To tackle this issue, we propose a novel method, named the Meta Similarity Importance Assignment Network (MSIAN), to achieve robust cross-modal retrieval. MSIAN employs a meta-learning strategy to dynamically learn the importance of each sample through a two-level optimization process. With adaptively guiding the learning process, MSIAN adjusts the importance weight of each sample based on its inherent trustworthiness. Thereby, thus iterative mechanism progressively shifts the network's focus on the most reliable data points, amplifying the impact of credible samples while diminishing the adaptive weight of noisy ones. Furthermore, MSIAN dynamically adapts the soft margin of each sample through continuously updated adaptive weights, thereby improving the robustness of the model. Extensive experiments on three widely used datasets, including Flickr30K, MS-COCO, and Conceptual Captions, demonstrate the effectiveness of our approach in improving cross-modal retrieval performance.