  Spatio-Temporal video grounding (STVG) focuses on retrieving the
spatio-temporal tube of a specific object depicted by a free-form textual
expression. Existing approaches mainly treat this complicated task as a
parallel frame-grounding problem and thus suffer from two types of
inconsistency drawbacks: feature alignment inconsistency and prediction
inconsistency. In this paper, we present an end-to-end one-stage framework,
termed Spatio-Temporal Consistency-Aware Transformer (STCAT), to alleviate
these issues. Specially, we introduce a novel multi-modal template as the
global objective to address this task, which explicitly constricts the
grounding region and associates the predictions among all video frames.
Moreover, to generate the above template under sufficient video-textual
perception, an encoder-decoder architecture is proposed for effective global
context modeling. Thanks to these critical designs, STCAT enjoys more
consistent cross-modal feature alignment and tube prediction without reliance
on any pre-trained object detectors. Extensive experiments show that our method
outperforms previous state-of-the-arts with clear margins on two challenging
video benchmarks (VidSTG and HC-STVG), illustrating the superiority of the
proposed framework to better understanding the association between vision and
natural language. Code is publicly available at
https://github.com/jy0205/STCAT.
