Referring expression grounding by multi-context reasoning

., appearance context and relationship context. While most existing approaches either ignore to explore the appearance details of the target region or rely on a manually designed reasoning structure and treat the context information of each neighboring object equivalently, inflexible to the scenario where referring expressions are complicated. In this paper, we put forward Multi-context Reasoning Network (MCRN) for referring expression grounding task, which can apply appearance context reasoning and relationship context reasoning simultaneously. Methodologically, for appearance context reasoning, we propose a local node attention to obtain local representation of the target object, which gives a more focus on its appearance details. For relationship context reasoning, we approach it as a language-guided multi-step reasoning problem and design a multi-step graph reasoning module to capture intra-context and inter-context between the target region of its intra-class and inter-class neighboring objects in an iterative way, which makes the reasoning process more reliable and interpretable. Our method demonstrates superiority based on extensive experimental outputs on three popular benchmark datasets.