Semantic Consistent Memory Hashing With Incremental Regularization for Incomplete Cross-Modal Retrieval

The explosive growth of multimedia data has established cross-modal retrieval as a critical research frontier, with cross-modal hashing (CMH) emerging as an effective technique for managing large-scale datasets. However, existing CMH methods typically rely on the unrealistic assumption that all multimodal training data are complete. This paper addresses the fundamental challenge of insufficient correlation information in incomplete multimodal scenarios, which manifests as two interconnected problems, i.e., intra-class dispersion caused by inadequate intra-modal correlation and limited inter-class separability resulting from insufficient cross-modal correlation. To overcome these limitations, we propose Semantic Consistent Memory Hashing with Incremental Regularization (SCMIR), a novel framework for incomplete cross-modal retrieval. Specifically, we first establish a robust semantic-consistent hash learning framework based on information bottleneck theory for complete paired samples, learning compact representations that preserve essential category information while eliminating modality-specific redundancy. We then introduce a memory bank-based incremental regularization mechanism that stores high-quality hash codes from complete data as semantic References, enabling effective knowledge transfer to incomplete samples through alignment constraints. Extensive experiments on three benchmark datasets demonstrate that SCMIR significantly outperforms state-of-the-art methods across various hash code lengths, achieving superior performance in both text-to-image and image-to-text retrieval tasks.