  Video Referring Expression Comprehension (REC) aims to localize a target
object in video frames referred by the natural language expression. Recently,
the Transformerbased methods have greatly boosted the performance limit.
However, we argue that the current query design is suboptima and suffers from
two drawbacks: 1) the slow training convergence process; 2) the lack of
fine-grained alignment. To alleviate this, we aim to couple the pure learnable
queries with the content information. Specifically, we set up a fixed number of
learnable bounding boxes across the frame and the aligned region features are
employed to provide fruitful clues. Besides, we explicitly link certain phrases
in the sentence to the semantically relevant visual areas. To this end, we
introduce two new datasets (i.e., VID-Entity and VidSTG-Entity) by augmenting
the VIDSentence and VidSTG datasets with the explicitly referred words in the
whole sentence, respectively. Benefiting from this, we conduct the fine-grained
cross-modal alignment at the region-phrase level, which ensures more detailed
feature representations. Incorporating these two designs, our proposed model
(dubbed as ContFormer) achieves the state-of-the-art performance on widely
benchmarked datasets. For example on VID-Entity dataset, compared to the
previous SOTA, ContFormer achieves 8.75% absolute improvement on<EMAIL_ADDRESS>