Given a short query with keyphrases about a research idea in 2018 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: In this paper, we present a method for Visual Question Answering. Our method first detects all objects

Keyphrases: visual question answering images

Dataset description:
CLEVR: CLEVR (Compositional Language and Elementary Visual Reasoning) is a synthetic Visual Question Answering dataset. It contains images of 3D-rendered objects; each image comes with a number of highly compositional questions that fall into different categories.
GuessWhat?!: GuessWhat?! is a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images.
VideoNavQA: The VideoNavQA dataset contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the Embodied Question Answering (EQA) task.
AQUA: 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.

Options:- A: CLEVR  B: GuessWhat?!  C: VideoNavQA  D: AQUA
CLEVR