Integrating Multi-Label Contrastive Learning With Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval

With the growing amount of multimodal data, cross-modal retrieval has attracted more and more attention and become a hot research topic. To date, most of the existing techniques mainly convert multimodal data into a common representation space where similarities in semantics between samples can be easily measured across multiple modalities. However, these approaches may suffer from the following limitations: 1) They overcome the modality gap by introducing loss in the common representation space, which may not be sufficient to eliminate the heterogeneity of various modalities; 2) They treat labels as independent entities and ignore label relationships, which is not conducive to establishing semantic connections across multimodal data; 3) They ignore the non-binary values of label similarity in multi-label scenarios, which may lead to inefficient alignment of representation similarity with label similarity. To tackle these problems, in this article, we propose two models to learn discriminative and modality-invariant representations for cross-modal retrieval. First, the dual generative adversarial networks are built to project multimodal data into a common representation space. Second, to model label relation dependencies and develop inter-dependent classifiers, we employ multi-hop graph neural networks (consisting of Probabilistic GNN and Iterative GNN), where the layer aggregation mechanism is suggested for using propagation information of various hops. Third, we propose a novel soft multi-label contrastive loss for cross-modal retrieval, with the soft positive sampling probability, which can align the representation similarity and the label similarity. Additionally, to adapt to incomplete-modal learning, which can have wider applications, we propose a modal reconstruction mechanism to generate missing features. Extensive experiments on three widely used benchmark datasets, i.e., NUS-WIDE, MIRFlickr, and MS-COCO, show the superiority of our proposed method.

Automated summary

With the increasing amount of multimodal data, cross-modal retrieval has become a hot research topic, but existing techniques have limitations in eliminating modality heterogeneity, considering label relationships, and efficiently aligning representation and label similarity. To address these problems, this article proposes two models that use dual generative adversarial networks to project multimodal data into a common representation space, employ multi-hop graph neural networks to model label relation dependencies, and introduce a novel soft multi-label contrastive loss to align representation and label similarity. Experimental results on three benchmark datasets demonstrate the superiority of the proposed method.