Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: The problem of visual question answering (VQA) is defined as finding the correct

Keyphrases: visual question answering image text

Dataset description:
VQA-CP: The VQA-CP dataset was constructed by reorganizing VQA v2 such that the correlation between the question type and correct answer differs in the training and test splits. For example, the most common answer to questions starting with What sport… is tennis in the training set, but skiing in the test set.
VizWiz: The VizWiz-VQA dataset originates from a natural visual question answering setting where blind people each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. The proposed challenge addresses the following two tasks for this dataset: predict the answer to a visual question and (2) predict whether a visual question cannot be answered.
VQA-E: VQA-E is a dataset for Visual Question Answering with Explanation, where the models are required to generate and explanation with the predicted answer. The VQA-E dataset is automatically derived from the VQA v2 dataset by synthesizing a textual explanation for each image-question-answer triple.
DAQUAR: DAQUAR (DAtaset for QUestion Answering on Real-world images) is a dataset of human question answer pairs about images.

Options:- A: VQA-CP  B: VizWiz  C: VQA-E  D: DAQUAR
DAQUAR