Adaptive Label-Aware Graph Convolutional Networks for Cross-Modal Retrieval

The cross-modal retrieval task has raised continuous attention in recent years with the increasing scale of multi-modal data, which has broad application prospects including multimedia data management and intelligent search engine. Most existing methods mainly project data of different modalities into a common representation space where label information is often exploited to distinguish samples from different semantic categories. However, they typically treat each label as an independent individual and ignore the underlying semantic structure of labels. In this paper, we propose an end-to-end adaptive label-aware graph convolutional network (ALGCN) by designing both the instance representation learning branch and the label representation learning branch, which can obtain modality-invariant and discriminative representations for cross-modal retrieval. Firstly, we construct an instance representation learning branch to transform instances of different modalities into a common representation space. Secondly, we adopt Graph Convolutional Network (GCN) to learn inter-dependent classifiers in the label representation learning branch. In addition, a novel adaptive correlation matrix is proposed to efficiently explore and preserve the semantic structure of labels in a data-driven manner. Together with a robust self-supervision loss for GCN, the GCN model can be supervised to learn an effective and robust correlation matrix for feature propagation. Comprehensive experimental results on three benchmark datasets, NUS-WIDE, MIRFlickr and MS-COCO, demonstrate the superiority of ALGCN, compared with the state-of-the-art methods in cross-modal retrieval.

Automated summary

In this paper, a novel end-to-end adaptive label-aware graph convolutional network (ALGCN) is proposed for cross-modal retrieval, achieving modality-invariant and discriminative representations by designing instance and label representation learning branches. The ALGCN model outperforms state-of-the-art methods in cross-modal retrieval on benchmark datasets like NUS-WIDE, MIRFlickr and MS-COCO.