  Given a video, video grounding aims to retrieve a temporal moment that
semantically corresponds to a language query. In this work, we propose a
Parallel Attention Network with Sequence matching (SeqPAN) to address the
challenges in this task: multi-modal representation learning, and target moment
boundary prediction. We design a self-guided parallel attention module to
effectively capture self-modal contexts and cross-modal attentive information
between video and text. Inspired by sequence labeling tasks in natural language
processing, we split the ground truth moment into begin, inside, and end
regions. We then propose a sequence matching strategy to guide start/end
boundary predictions using region labels. Experimental results on three
datasets show that SeqPAN is superior to state-of-the-art methods. Furthermore,
the effectiveness of the self-guided parallel attention module and the sequence
matching module is verified.
