  Spatio-temporal video grounding aims to retrieve the spatio-temporal tube of
a queried object according to the given sentence. Currently, most existing
grounding methods are restricted to well-aligned segment-sentence pairs. In
this paper, we explore spatio-temporal video grounding on unaligned data and
multi-form sentences. This challenging task requires to capture critical object
relations to identify the queried target. However, existing approaches cannot
distinguish notable objects and remain in ineffective relation modeling between
unnecessary objects. Thus, we propose a novel object-aware multi-branch
relation network for object-aware relation discovery. Concretely, we first
devise multiple branches to develop object-aware region modeling, where each
branch focuses on a crucial object mentioned in the sentence. We then propose
multi-branch relation reasoning to capture critical object relationships
between the main branch and auxiliary branches. Moreover, we apply a diversity
loss to make each branch only pay attention to its corresponding object and
boost multi-branch learning. The extensive experiments show the effectiveness
of our proposed method.
