Robust zero-shot discrete hashing with noisy labels for cross-modal retrieval

Recently, zero-shot hashing methods have been successfully applied to cross-modal retrieval. However, these methods typically assume that the training data labels are accurate and noise-free, which is unrealistic in real-world scenarios due to the noises introduced by manual or automatic annotation. To address this problem, we propose a robust zero-shot discrete hashing with noisy labels (RZSDH) method, which fully considers the impact of noisy labels in real scenes. Our RZSDH method incorporates the sparse and low-rank constraints on the noise matrix and the recovered label matrix, respectively, to effectively reduce the negative impact of noisy labels. Therefore, this significantly enhances the robustness of our proposed method in practice cross-modal retrieval tasks. Additionally, the proposed RZSDH method learns a representation vector of each category attribute, which effectively captures the relationship between seen classes and unseen classes. Furthermore, our approach learns the common latent representation with drift from multimodal data features, which is more conducive to obtaining stable hash codes and hash functions. Finally, we employ a fine-grained similarity preserving strategy to generate more discriminative hash codes. Experiments on several benchmark datasets verify the effectiveness and robustness of the proposed RZSDH method.