  Referring expression grounding aims at locating certain objects or persons in
an image with a referring expression, where the key challenge is to comprehend
and align various types of information from visual and textual domain, such as
visual attributes, location and interactions with surrounding regions. Although
the attention mechanism has been successfully applied for cross-modal
alignments, previous attention models focus on only the most dominant features
of both modalities, and neglect the fact that there could be multiple
comprehensive textual-visual correspondences between images and referring
expressions. To tackle this issue, we design a novel cross-modal
attention-guided erasing approach, where we discard the most dominant
information from either textual or visual domains to generate difficult
training samples online, and to drive the model to discover complementary
textual-visual correspondences. Extensive experiments demonstrate the
effectiveness of our proposed method, which achieves state-of-the-art
performance on three referring expression grounding datasets.
