Two-stream Hierarchical Similarity Reasoning for Image-text Matching

• URL: http://arxiv.org/abs/2203.05349v1
• Date: Thu, 10 Mar 2022 12:56:10 GMT
• Title: Two-stream Hierarchical Similarity Reasoning for Image-text Matching
• Authors: Ran Chen, Hanli Wang, Lei Wang, Sam Kwong
• Abstract summary: A hierarchical similarity reasoning module is proposed to automatically extract context information. Previous approaches only consider learning single-stream similarity alignment. A two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation.
• Score: 66.43071159630006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods.

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