Retrieval-Guided Completion Hashing with Token–Patch Alignment for Incomplete Cross-Modal Retrieval

Abstract

Cross-modal hashing has been extensively adopted in cross-modal retrieval tasks owing to its high storage efficiency and fast retrieval capability. However, in practical applications, multimodal data often suffer from modality missing issues, which cause semantic incompleteness and thus severely impair both cross-modal alignment and hashing-based retrieval performance. To address these issues, this paper proposes a novel incomplete cross-modal retrieval framework, called retrieval-guided completion hashing with token–patch alignment (RGCH-TPA). Specifically, it first constructs a dynamic memory bank based on the features of complete samples and uses available modalities to retrieve and locate similar regions of incomplete samples. Thus, it achieves intra-modal reconstruction of missing data under the condition of available modalities using retrieved expert signals. Moreover, a token-patch level cross-modal alignment mechanism is introduced to model the interaction between text tokens and image patches, while global representations are jointly integrated to further enhance the discriminability and robustness of the learned cross-modal representations. Extensive evaluations on three benchmark datasets demonstrate that the proposed RGCH-TPA method consistently outperforms other state-of-the-art incomplete cross-modal hashing methods across missing-modality ratios. The source code is available at https://github.com/szq0816/RGCH-TPA.