Robust Memory-guided Hashing for Cross-modal Retrieval with Noisy Labels

Cross-modal hashing (CMH) provides an efficient solution for large-scale multimodal retrieval, but its performance is vulnerable to noisy labels. Existing noise-robust CMH methods typically estimate sample or label reliability based on signals derived from the current model, such as training losses, prediction confidence, neighborhood relations, or learned semantic representations. However, these signals are themselves affected by noisy supervision, and biases in reliability estimation can be progressively amplified during iterative optimization, leading to noise propagation and error accumulation. To address this issue, we propose Robust Memory-guided Hashing (RMH), which exploits semantic information accumulated from historically reliable samples to provide stable references for current reliability estimation and hash learning. Specifically, Memory-guided Reliability Estimation (MRE) constructs modality-specific historical class centers from historically reliable samples, estimates the reliability of current samples through bidirectional cross-modal matching, and employs a smooth threshold to partition samples into reliable and unreliable subsets while updating the memory only with reliable samples. Furthermore, we develop Reliable Cross-batch Learning (RCL), which jointly exploits in-batch and historical cross-batch semantic relations for learning from reliable samples, while introducing an uncertainty-aware loss to attenuate potentially erroneous supervision associated with unreliable samples, thereby improving data utilization and the robustness of hash representations. Extensive experiments under multiple noise patterns and noise rates demonstrate that RMH achieves consistently competitive retrieval performance.