  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
(ii) a Recurrent Aggregation Regulator (RAR) which adjusts the aggregation
weights repeatedly to increasingly emphasize important alignments and dilute
unimportant ones. Besides, it is interesting that RCR and RAR are
plug-and-play: both of them can be incorporated into many frameworks based on
cross-modal interaction to obtain significant benefits, and their cooperation
achieves further improvements. Extensive experiments on MSCOCO and Flickr30K
datasets validate that they can bring an impressive and consistent R@1 gain on
multiple models, confirming the general effectiveness and generalization
ability of the proposed methods. Code and pre-trained models are available at:
https://github.com/Paranioar/RCAR.
