Cross-Domain Cross-Modal Hashing via Semantic Relation Transfer and Inverse-Free Broad Learning

Cross-domain cross-modal hashing aims to leverage semantically rich knowledge from an auxiliary domain to improve cross-modal retrieval in an unlabeled or weakly labeled target domain. Existing methods face two major challenges: the lack of reliable semantic supervision in the target domain, and the increased complexity of cross-domain transfer caused by modality discrepancies and disjoint label spaces, which further impedes the exploitation of semantic information. To address these challenges, we propose a hashing framework based on semantic relation transfer and QR-based incremental learning. For cross-domain semantic transfer, we learn transferable cross-modal representations from the semantically rich auxiliary domain and bridge the auxiliary and target domains through parameter transfer and cross-domain semantically aligned samples. Meanwhile, we construct an auxiliary-domain semantic relation graph and a weak semantic relation graph for the target domain using CLIP-generated pseudo-labels. By jointly modeling semantic relations across the two domains, our method provides effective semantic guidance for the supervision-deficient target domain, thereby facilitating knowledge transfer and target-domain adaptation. For representation learning, we introduce an incrementally extensible broad learning-based hashing layer that dynamically expands the representation space by appending feature nodes and enhancement nodes. We further develop an incremental QR decomposition-based update of the R-factor and employ triangular solves to analytically compute the expanded-layer weights, eliminating the need for the explicit pseudoinverse computation required by conventional broad learning methods. By integrating auxiliary-to-target knowledge transfer with incremental target-domain learning in a lightweight hashing framework, together with dual semantic relation graph modeling, the proposed method simultaneously enables cross-domain knowledge transfer and target-domain semantic enhancement. Extensive experiments on two benchmark datasets demonstrate significant improvements over existing methods, validating the effectiveness and superiority of the proposed framework.