A Dataset and Baselines for Visual Question Answering on Art
This work addresses the challenge of answering questions about art for researchers in computer vision and AI, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of visual question answering on art by introducing the AQUA dataset, which includes automatically generated and crowdsourced QA pairs for paintings, and they presented a two-branch baseline model that achieved competitive performance against state-of-the-art models, though specific numbers were not provided.
Answering questions related to art pieces (paintings) is a difficult task, as it implies the understanding of not only the visual information that is shown in the picture, but also the contextual knowledge that is acquired through the study of the history of art. In this work, we introduce our first attempt towards building a new dataset, coined AQUA (Art QUestion Answering). The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. Our dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions. We also present a two-branch model as baseline, where the visual and knowledge questions are handled independently. We extensively compare our baseline model against the state-of-the-art models for question answering, and we provide a comprehensive study about the challenges and potential future directions for visual question answering on art.