SemArt is a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections. It contains 21,384 samples that provides artistic comments along with fine-art paintings and their attributes for studying semantic art understanding. Source: How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval
The task of Visual Question Answering (VQA) has been studied extensively on general-domain real-world images. Transferring insights from general domain VQA to the art domain (ArtVQA) is non-trivial, as the latter requires models to identify abstract concepts, details of brushstrokes and styles of paintings in the visual data as well as possess background knowledge about art. This is exacerbated by the lack of high-quality datasets. In this work, we shed light on hidden linguistic biases in the AQUA dataset, which is the only publicly available benchmark dataset for ArtVQA. As a result, the majority of questions can be answered without consulting the visual information, making the “V” in ArtVQA rather insignificant. In order to counter this problem, we create a simple, yet practical dataset, ArtQuest, using structured information from the SemArt collection. Our dataset and the pipeline to reproduce our results are publicly available at https://github.com/bletib/artquest.