Plug-and-Play Regulators for Image-Text Matching
Haiwen Diao, Ying Zhang, Wei Liu, Xiang Ruan, Huchuan Lu

TL;DR
This paper introduces two simple, effective plug-and-play regulators for image-text matching that enhance cross-modal representations by adaptively capturing and emphasizing important alignments, leading to improved performance across multiple models.
Contribution
The paper proposes Recurrent Correspondence Regulator and Recurrent Aggregation Regulator, novel modules that improve image-text matching by adaptive, iterative encoding of cross-modal interactions.
Findings
Significant R@1 improvements on MSCOCO and Flickr30K datasets.
Both regulators are plug-and-play and improve various existing frameworks.
The methods demonstrate strong generalization and effectiveness.
Abstract
Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. Generally, recent approaches first employ a cross-modal attention unit to capture latent region-word interactions, and then integrate all the alignments to obtain the final similarity. However, most of them adopt one-time forward association or aggregation strategies with complex architectures or additional information, while ignoring the regulation ability of network feedback. In this paper, we develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Specifically, we propose (i) a Recurrent Correspondence Regulator (RCR) which facilitates the cross-modal attention unit progressively with adaptive attention factors to capture more flexible correspondence, and…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques · Video Analysis and Summarization
