  We investigate the problem of video Referring Expression Comprehension (REC),
which aims to localize the referent objects described in the sentence to visual
regions in the video frames. Despite the recent progress, existing methods
suffer from two problems: 1) inconsistent localization results across video
frames; 2) confusion between the referent and contextual objects. To this end,
we propose a novel Dual Correspondence Network (dubbed as DCNet) which
explicitly enhances the dense associations in both the inter-frame and
cross-modal manners. Firstly, we aim to build the inter-frame correlations for
all existing instances within the frames. Specifically, we compute the
inter-frame patch-wise cosine similarity to estimate the dense alignment and
then perform the inter-frame contrastive learning to map them close in feature
space. Secondly, we propose to build the fine-grained patch-word alignment to
associate each patch with certain words. Due to the lack of this kind of
detailed annotations, we also predict the patch-word correspondence through the
cosine similarity. Extensive experiments demonstrate that our DCNet achieves
state-of-the-art performance on both video and image REC benchmarks.
Furthermore, we conduct comprehensive ablation studies and thorough analyses to
explore the optimal model designs. Notably, our inter-frame and cross-modal
contrastive losses are plug-and-play functions and are applicable to any video
REC architectures. For example, by building on top of Co-grounding, we boost
the performance by 1.48% absolute improvement on<EMAIL_ADDRESS>for VID-Sentence
dataset.
