Multi-level expression guided attention network for referring expression comprehension

Referring expression comprehension is a task of identifying a text-related object or region in a given image by a natural language expression. In this task, it is essential to understand the expression sentence in multi-aspect and adapt it to region representations for generating the discriminative information. Unfortunately, previous approaches usually focus on the important words or phrases in the expression using self-attention mechanisms, which causes that they may fail to distinguish the target region from others, especially the similar regions. To address this problem, we propose a novel model, termed Multi-level Expression Guided Attention network (MEGA-Net). It contains a multi-level visual attention schema guided by the expression representations in different levels, i.e., sentence-level, word-level and phrase-level, which allows generating the discriminative region features and helps to locate the related regions accurately. In addition, to distinguish the similar regions, we design a two-stage structure, where we first select top-K candidate regions according to their matching scores in the first stage, then we apply an object comparison attention mechanism to learn the difference between the candidates for matching the target region. We evaluate the proposed approach on three popular benchmark datasets and the experimental results demonstrate that our model performs against state-of-the-art methods.