InterREC: An Interpretable Method for Referring Expression Comprehension

Referring Expression Comprehension (REC) aims to locate the target object in the image according to a referring expression. This is a challenging task owing to the need for understanding both natural language and visual information and interpretable reasoning between them. Most existing implicit reasoning-based REC methods lack interpretability, while explicit reasoning-based REC methods have lower accuracy. To achieve competitive accuracy while providing adequate interpretability, in this work, we propose a novel explicit reasoning-based method named InterREC. First, in order to address the challenge of multi-modal understanding, we design two neural network modules based on text-image representation learning: a Text-Region Matching Module to align objects in the image and noun phrases in the expression, and a Text-Relation Matching Module to align relations between objects in the image and relational phrases in the expression. Additionally, we design a Reasoning Order Tree for handling complex expressions, which can reduce complex expressions to multiple object-relation-object triplets and therefore identify the inference order and reduce the difficulty of reasoning. At the same time, to achieve an interpretable reasoning step, we design a Bayesian Network-based explicit reasoning method. Based on the comparative evaluation on various datasets, our method achieves higher accuracy than existing explicit reasoning-based REC methods, and the visualization results demonstrate the method's high interpretability.