  In this technical report, we introduce our solution to human-centric
spatio-temporal video grounding task. We propose a concise and effective
framework named STVGFormer, which models spatiotemporal visual-linguistic
dependencies with a static branch and a dynamic branch. The static branch
performs cross-modal understanding in a single frame and learns to localize the
target object spatially according to intra-frame visual cues like object
appearances. The dynamic branch performs cross-modal understanding across
multiple frames. It learns to predict the starting and ending time of the
target moment according to dynamic visual cues like motions. Both the static
and dynamic branches are designed as cross-modal transformers. We further
design a novel static-dynamic interaction block to enable the static and
dynamic branches to transfer useful and complementary information from each
other, which is shown to be effective to improve the prediction on hard cases.
Our proposed method achieved 39.6% vIoU and won the first place in the HC-STVG
track of the 4th Person in Context Challenge.
