Multi-scale relation reasoning for multi-modal Visual Question Answering

Abstract

The goal of Visual Question Answering (VQA) is to answer questions about images. For the same picture, there are often completely different types of questions. Therefore, the main difficulty of VQA task lies in how to properly reason relationships among multiple visual objects according to types of input questions. To solve this difficulty, this paper proposes a deep neural network to perform multi-modal relation reasoning in multi-scales, which successfully constructs a regional attention scheme to focus on informative and question-related regions for better answering. Specifically, we firstly design regional attention scheme to select regions of interest based on informative evaluation computed by a question-guided soft attention module. Afterwards, features computed by regional attention scheme are fused in scaled combinations, thus generating more distinctive features with scalable information. Due to designs of regional attention and multi-scale property, the proposed method is capable to describe scaled relationships from multi-modal inputs to offer accurate question-guided answers. By conducting experiments on VQA v1 and VQA v2 datasets, we show that the proposed method has superior efficiencies than most of the existing methods.