Relation-Aware Graph Attention Network for Visual Question Answering

Linjie Li, Zhe Gan, Yu Cheng, Jingjing Liu; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 10313-10322

Abstract


In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into a graph and models multi-type inter-object relations via a graph attention mechanism, to learn question-adaptive relation representations. Two types of visual object relations are explored: (i) Explicit Relations that represent geometric positions and semantic interactions between objects; and (ii) Implicit Relations that capture the hidden dynamics between image regions. Experiments demonstrate that ReGAT outperforms prior state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further show that ReGAT is compatible to existing VQA architectures, and can be used as a generic relation encoder to boost the model performance for VQA.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Li_2019_ICCV,
author = {Li, Linjie and Gan, Zhe and Cheng, Yu and Liu, Jingjing},
title = {Relation-Aware Graph Attention Network for Visual Question Answering},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}