Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question
  Answering
Yi Cheng Hehe Fan Dongyun Lin Ying Sun Mohan Kankanhalli Joo-Hwee Lim
The main challenge in video question answering (VideoQA) is to capture and
understand the complex spatial and temporal relations between objects based on
given questions. Existing graph-based methods for VideoQA usually ignore
keywords in questions and employ a simple graph to aggregate features without
considering relative relations between objects, which may lead to inferior
performance. In this paper, we propose a Keyword-aware Relative Spatio-Temporal
(KRST) graph network for VideoQA. First, to make question features aware of
keywords, we employ an attention mechanism to assign high weights to keywords
during question encoding. The keyword-aware question features are then used to
guide video graph construction. Second, because relations are relative, we
integrate the relative relation modeling to better capture the spatio-temporal
dynamics among object nodes. Moreover, we disentangle the spatio-temporal
reasoning into an object-level spatial graph and a frame-level temporal graph,
which reduces the impact of spatial and temporal relation reasoning on each
other. Extensive experiments on the TGIF-QA, MSVD-QA and MSRVTT-QA datasets
demonstrate the superiority of our KRST over multiple state-of-the-art methods.