Bongini et al. [13] annotated a subset of the Artpedia dataset with visual and contextual question-answer pairs. They
introduced a question classiﬁer that discriminates between visual and contextual questions and a model capable of
answering both types of questions. Garcia et al. [49] presented a novel dataset AQUA, which consists of automatically
generated visual- and knowledge-based QA pairs, and introduced a two-branch model where the visual and knowledge
questions are managed independently. Apart from VAQ, a few recent works addressed the task of image captioning
where the goal is to automatically generate accurate textual descriptions of images. Sheng and Moens [112] introduce
image captioning datasets referring to ancient Egyptian and Chinese art and employ an encoder-decoder framework
for image captioning where the encoder is a CNN and the decoder is a long short-term memory (LSTM) network.
UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK