Globally Guided Confidence Enhancement Network for Image-Text Matching

Abstract

Image-text matching is a crucial aspect of multi-modal intelligence. The main challenge in this area is accurately measuring the relevance between the image and text, using evidence obtained through matching. Previous studies either concentrated on obtaining a well-represented global feature to measure similarity directly or on investigating complex matching patterns at a local level before aggregating them, with little attention paid to combining them. We propose a Globally Guided Confidence Enhancement Network that combines both approaches by obtaining a good global representation to guide fine-grained local interactions. In this process, content that better matches the text from a global perspective is enhanced and represented with confidence scores. Extensive experiments demonstrate that the approach we have employed achieves superior performance on Flickr30K and MSCOCO datasets.