Explicit Knowledge Integration for Knowledge-Aware Visual Question Answering about Named Entities

Recent years have shown unprecedented growth of interest in Vision-Language related tasks, with the need to address the inherent challenges of integrating linguistic and visual information to solve real-world applications. Such a typical task is Visual Question Answering (VQA), which aims to answer questions about visual content. The limitations of the VQA task in terms of question redundancy and poor linguistic variability encouraged researchers to propose Knowledge-aware Visual Question Answering tasks as a natural extension of VQA. In this paper, we tackle the KVQAE (Knowledge-based Visual Question Answering about named Entities) task, which proposes to answer questions about named entities defined in a knowledge base and grounded in visual content. In particular, besides the textual and visual information, we propose to leverage the structural information extracted from syntactic dependency trees and external knowledge graphs to help answer questions about a large spectrum of entities of various types. Thus, by combining contextual and graph-based representations using Graph Convolutional Networks (GCNs), we are able to learn meaningful embeddings for Information Retrieval tasks. Experiments on the ViQuAE public dataset show how our approach improves the state-of-the-art baselines while demonstrating the interest of injecting external knowledge to enhance multimodal information retrieval.