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.

Image-text retrieval is a fundamental cross-modal task that aims to align the representation spaces between the image and text modalities. Existing cross-modal image-text retrieval methods independently generate embeddings for images and text, introduce interaction-based networks for cross-modal inference, and then achieve retrieval by using matching metrics. However, they overlook the semantic relationship between the coarse-grained and fine-grained representations within each modality, failing to capture the consistency of representations across different modalities, which affects the semantic learning of cross-modal representations, and makes it difficult to align modalities in semantic space. Consequently, these previous works inevitably suffer from low retrieval accuracy or high computational costs. In this paper, instead of directly fusing two cross-modal het-erogeneous spaces, we propose an multimodal knowledge enhanced multimodal transformer network framework to combine coarse-grained and fine-grained representation learning into a unified framework, capturing alignment information between targets, constructing a global semantic graph, and ultimately align multimodal representations in the semantic space. In our approach, images generate semantic and spatial graphs to represent visual information, while sentences generate text graphs based on semantic relationships between words, and they are used for intra-modal graph network inference. Subsequently, the generated global and local embeddings are fused into an enhanced multimodal transformer framework, effectively imple-menting cross-modal interaction processes by leveraging prior implicit semantic information from the multimodal knowledge graph. Furthermore, compared to simply matching words with image regions, our method proposes a bidirectional fine-grained matching method to filter the salient regions and words of images and texts, remove the interfering noise information, and realize bidirectional fine-grained pairing, which captures fine-grained bi-directional representational information, thus enable the model to generate more discriminative representations Finally, equipped with a coarse-to-fine inference method based on hybrid global and local cross-modal similarities, we demonstrate that the proposed method is able to significantly outperform existing state-of-the-art algorithms by evaluating our method using two widely-used datasets.