  In this paper, we propose a novel graph learning framework for phrase
grounding in the image. Developing from the sequential to the dense graph
model, existing works capture coarse-grained context but fail to distinguish
the diversity of context among phrases and image regions. In contrast, we pay
special attention to different motifs implied in the context of the scene graph
and devise the disentangled graph network to integrate the motif-aware
contextual information into representations. Besides, we adopt interventional
strategies at the feature and the structure levels to consolidate and
generalize representations. Finally, the cross-modal attention network is
utilized to fuse intra-modal features, where each phrase can be computed
similarity with regions to select the best-grounded one. We validate the
efficiency of disentangled and interventional graph network (DIGN) through a
series of ablation studies, and our model achieves state-of-the-art performance
on Flickr30K Entities and ReferIt Game benchmarks.
