Graph Structured Network for Image-Text Matching
This work addresses the challenge of fine-grained image-text matching for applications in vision-language tasks, representing an incremental advance over existing methods that rely on coarse object co-occurrence.
The paper tackles the problem of learning fine-grained correspondence between images and text by proposing a Graph Structured Matching Network (GSMN), which models objects, relations, and attributes as structured phrases and achieves relative Recall@1 improvements of nearly 7% on Flickr30K and 2% on MSCOCO compared to state-of-the-art methods.
Image-text matching has received growing interest since it bridges vision and language. The key challenge lies in how to learn correspondence between image and text. Existing works learn coarse correspondence based on object co-occurrence statistics, while failing to learn fine-grained phrase correspondence. In this paper, we present a novel Graph Structured Matching Network (GSMN) to learn fine-grained correspondence. The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase. This is achieved by node-level matching and structure-level matching. The node-level matching associates each node with its relevant nodes from another modality, where the node can be object, relation or attribute. The associated nodes then jointly infer fine-grained correspondence by fusing neighborhood associations at structure-level matching. Comprehensive experiments show that GSMN outperforms state-of-the-art methods on benchmarks, with relative Recall@1 improvements of nearly 7% and 2% on Flickr30K and MSCOCO, respectively. Code will be released at: https://github.com/CrossmodalGroup/GSMN.