Conventional representation learning based cross-modal retrieval approaches always represent the sentence with a global embedding feature, which easily neglects the local correlations between objects in the image and phrases in the sentence. In this paper, we present a novel Multi-hop Interactive Cross-modal Retrieval Model (MICRM), which interactively exploits the local correlations between images and words. We design a multi-hop interactive module to infer the high-order relevance between the image and the sentence. Experimental results on two benchmark datasets, MS-COCO and Flickr30K, demonstrate that our multi-hop interactive model performs significantly better than several competitive cross-modal retrieval methods.
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