External Guidance Incomplete Cross-Modal Hashing

Cross-modal hashing (CMH) aims to bridge the semantic gap between heterogeneous modalities by learning compact binary representations for efficient retrieval. Most existing deep cross-modal hashing methods are developed under the assumption that multimodal data are complete and perfectly paired across modalities. However, this assumption rarely holds as real-world multimodal datasets often suffer from missing modalities due to inconsistencies, imbalances, or noise during data collection. To address such incomplete data, existing incomplete CMH methods typically attempt to reconstruct the missing information by exploiting internal signals from the available modalities. Nonetheless, these internally guided completion strategies tend to be highly sensitive to distributional shifts, leading to substantial performance degradation on unseen or out-of-distribution data. Inspired by the human learning mechanism of enhancing cognition through external knowledge, this paper proposes a novel External Guidance Incomplete Cross-modal Hashing (EGICH) framework to address this limitation. Specifically, we first design a Completion with External Guidance (CEG) module that leverages rich semantic information from external knowledge bases to expand the semantic boundary and accurately reconstruct the semantics of missing samples. Subsequently, we introduce a Consistency Learning with External Guidance (CLEG) module, which employs externally guided reconstructed features as anchors to align sample representations with label semantics, thereby effectively mitigating cross-modal bias. Finally, a Semantic-aware Contrastive Hashing (SCH) module is developed to refine the feature distribution by semantic similarity, pulling semantically related samples closer and pushing unrelated ones apart, thus achieving fine-grained discrimination among positive pairs. To the best of our knowledge, this is the first attempt to incorporate external knowledge into incomplete cross-modal hashing. Extensive experiments demonstrate that EGICH consistently and significantly outperforms 11 state-of-the-art methods under various modality-missing scenarios. The code is available at https://github.com/chenjiali27/EGICH.