  Despite the achievements of large-scale multimodal pre-training approaches,
cross-modal retrieval, e.g., image-text retrieval, remains a challenging task.
To bridge the semantic gap between the two modalities, previous studies mainly
focus on word-region alignment at the object level, lacking the matching
between the linguistic relation among the words and the visual relation among
the regions. The neglect of such relation consistency impairs the
contextualized representation of image-text pairs and hinders the model
performance and the interpretability. In this paper, we first propose a novel
metric, Intra-modal Self-attention Distance (ISD), to quantify the relation
consistency by measuring the semantic distance between linguistic and visual
relations. In response, we present Inter-modal Alignment on Intra-modal
Self-attentions (IAIS), a regularized training method to optimize the ISD and
calibrate intra-modal self-attentions from the two modalities mutually via
inter-modal alignment. The IAIS regularizer boosts the performance of
prevailing models on Flickr30k and MS COCO datasets by a considerable margin,
which demonstrates the superiority of our approach.
