Robust Unsupervised Cross-Modal Hashing via Causal Subject-Context Intervention

In unsupervised cross-modal hashing, similarity relations are used to guide hash learning and thus directly shape the learned Hamming space. Although existing methods seek to obtain more reliable similarity relations through various estimation and refinement strategies, they generally derive such relations from representations where retrieval-relevant subject semantics may still be entangled with contextual factors, such as shared backgrounds, textual styles, or incidental co-occurrences. Consequently, context-induced associations may be encoded as semantic similarities and propagated through iterative similarity updates, thereby misleading subsequent hash learning. To address this issue, we propose Causal Subject-Context Intervention Hashing (CSCIH). Specifically, Causal Subject-Context Intervention (CSCI) learns subject- and context-oriented representations and performs an approximate differentiable intervention by restricting residual refinement to context-oriented dimensions while preserving subject-oriented ones. It further evaluates whether subject information remains consistent across alternative contextual states and summarizes this evidence as a subject consistency score. Moreover, CSCIH derives complementary similarity relations from subject-oriented representations and continuous hash representations. Health-Aware Structure Refresh (HASR) adaptively balances these relations according to subject consistency and training progress, enabling more reliable similarity updates throughout training. Finally, Geometry-Aware Joint Hash Learning (GJHL) captures modality-local and cross-modal relational structures in both Euclidean and hyperbolic spaces and integrates them into a continuous teacher representation to guide modality-specific hash learning. Experiments on three benchmark datasets demonstrate that CSCIH consistently outperforms representative unsupervised cross-modal hashing methods.