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arXiv · 2303.11090

Scene Graph Based Fusion Network For Image-Text Retrieval

Guoliang Wang·Yanlei Shang·Yong Chen
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Abstract

A critical challenge to image-text retrieval is how to learn accurate correspondences between images and texts. Most existing methods mainly focus on coarse-grained correspondences based on co-occurrences of semantic objects, while failing to distinguish the fine-grained local correspondences. In this paper, we propose a novel Scene Graph based Fusion Network (dubbed SGFN), which enhances the images'/texts' features through intra- and cross-modal fusion for image-text retrieval. To be specific, we design an intra-modal hierarchical attention fusion to incorporate semantic contexts, such as objects, attributes, and relationships, into images'/texts' feature vectors via scene graphs, and a cross-modal attention fusion to combine the contextual semantics and local fusion via contextual vectors. Extensive experiments on public datasets Flickr30K and MSCOCO show that our SGFN performs better than quite a few SOTA image-text retrieval methods.

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Guoliang Wang, Yanlei Shang, Yong Chen. 2023-03-20. Scene Graph Based Fusion Network For Image-Text Retrieval. https://arxiv.org/abs/2303.11090

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