Asymmetric Pre-aligned Anchor Contrastive Enhanced Diffusion Hashing Model for Incomplete Multimodal Retrieval

Multimodal hashing stands as an efficient approach for multimodal retrieval, yet it frequently grapples with the challenge of misaligned representation spaces across different modalities. This misalignment can degrade the consistency and discrimination of multimodal representations, complicating the learning of effective representations for image and text pairs. Particularly, the task becomes arduous when the system must handle incomplete data while ensuring accurate and relevant retrieval outcomes. To address these challenges, we propose the Asymmetric Pre-aligned Anchor Contrastive Enhanced Diffusion Hashing Model (AADH) for Incomplete Multimodal Retrieval. Our model is specifically tailored to robustly manage multimodal incomplete data scenarios. Initially, we develop an Asymmetric Pre-alignment Strategy that utilizes asymmetric contrastive learning to preliminarily align the semantic disparities between various modalities. Subsequently, we propose an innovative Anchor Contrastive Reinforcement Diffusion Hashing Model, which integrates image and text modalities to varying extents during the reverse diffusion process. It constructs an anchor space that not only facilitates the learning of incomplete multimodal hashing representations through anchor contrastive learning but also leverages inter-modal and intra-modal contrastive learning to enhance the representations. Moreover, we effectively bridge the modality gap between different modal hash codes by employing the anchor space to constrain the representations of different modal hashes. By adjusting the initial noise of the diffusion model, we indirectly expand the data volume, which in turn bolsters the model's robustness. Our extensive experimental results across multiple datasets demonstrate that the proposed AADH model achieves state-of-the-art (SOTA) results.