Discrete Elective Hashing with Incomplete Labels for Efficient Cross-Modal Retrieval

Recently, supervised cross-modal hashing methods have gained considerable attention due to their ability to mine credible semantic relationships between multi-modal data. These methods typically rely on labels to explore semantic relationships provided that labels are always reliable, which, however, may not be true from the practical perspective. In fact, labels may be incomplete, i.e., true-label incomplete and fine-grained incomplete, which makes the performance of the existing methods deteriorated. To this end, we propose a method called Discrete Elective Hashing with Incomplete Labels (DEH-IL), which is designed to alleviate the impact of incomplete labels. Specifically, we introduce a relaxed label scheme that allows the algorithm to automatically mine potential missing information from incomplete labels, which is beneficial for exploring intra-class relationships. Moreover, we propose a novel elective loss that aggregates all estimations from incomplete labels to mine inter-class relationships. Since elective loss does not rely on any single estimation, it can effectively mitigate estimation errors arising from incomplete labels. By combining these two components, DEH-IL can effectively explore both intra-class and inter-class relationships through incomplete labels. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method.