  Spatial-Temporal Video Grounding (STVG) is a challenging task which aims to
localize the spatio-temporal tube of the interested object semantically
according to a natural language query. Most previous works not only severely
rely on the anchor boxes extracted by Faster R-CNN, but also simply regard the
video as a series of individual frames, thus lacking their temporal modeling.
Instead, in this paper, we are the first to propose an anchor-free framework
for STVG, called Gaussian Kernel-based Cross Modal Network (GKCMN).
Specifically, we utilize the learned Gaussian Kernel-based heatmaps of each
video frame to locate the query-related object. A mixed serial and parallel
connection network is further developed to leverage both spatial and temporal
relations among frames for better grounding. Experimental results on VidSTG
dataset demonstrate the effectiveness of our proposed GKCMN.
