  This paper revisits visual representation in knowledge-based visual question
answering (VQA) and demonstrates that using regional information in a better
way can significantly improve the performance. While visual representation is
extensively studied in traditional VQA, it is under-explored in knowledge-based
VQA even though these two tasks share the common spirit, i.e., rely on visual
input to answer the question. Specifically, we observe that in most
state-of-the-art knowledge-based VQA methods: 1) visual features are extracted
either from the whole image or in a sliding window manner for retrieving
knowledge, and the important relationship within/among object regions is
neglected; 2) visual features are not well utilized in the final answering
model, which is counter-intuitive to some extent. Based on these observations,
we propose a new knowledge-based VQA method REVIVE, which tries to utilize the
explicit information of object regions not only in the knowledge retrieval
stage but also in the answering model. The key motivation is that object
regions and inherent relationship are important for knowledge-based VQA. We
perform extensive experiments on the standard OK-VQA dataset and achieve new
state-of-the-art performance, i.e., 58.0% accuracy, surpassing previous
state-of-the-art method by a large margin (+3.6%). We also conduct detailed
analysis and show the necessity of regional information in different framework
components for knowledge-based VQA. Code is publicly available at
https://github.com/yzleroy/REVIVE.
