Learning Hierarchical Semantic Correspondences for Cross-Modal Image-Text Retrieval

Abstract

Cross-modal image-text retrieval is a fundamental task in information retrieval. The key to this task is to address both heterogeneity and cross-modal semantic correlation between data of different modalities. Fine-grained matching methods can nicely model local semantic correlations between image and text but face two challenges. First, images may contain redundant information while text sentences often contain words without semantic meaning. Such redundancy interferes with the local matching between textual words and image regions. Furthermore, the retrieval shall consider not only low-level semantic correspondence between image regions and textual words but also a higher semantic correlation between different intra-modal relationships. We propose a multi-layer graph convolutional network with object-level, object-relational-level, and higher-level learning sub-networks. Our method learns hierarchical semantic correspondences by both local and global alignment. We further introduce a self-attention mechanism after the word embedding to weaken insignificant words in the sentence and a cross-attention mechanism to guide the learning of image features. Extensive experiments on Flickr30K and MS-COCO datasets demonstrate the effectiveness and superiority of our proposed method.