Given a short query with keyphrases about a research idea in 2017 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: We present a question answering approach for video content. The proposed method encodes videos into feature representations and

Keyphrases: video question answering

Dataset description:
Visual7W: Visual7W is a large-scale visual question answering (QA) dataset, with object-level groundings and multimodal answers. Each question starts with one of the seven Ws, what, where, when, who, why, how and which.
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.
TVQA: The TVQA dataset is a large-scale vido dataset for video question answering. It is based on 6 popular TV shows (Friends, The Big Bang Theory, How I Met Your Mother, House M.D., Grey's Anatomy, Castle).
SWAG: Given a partial description like "she opened the hood of the car," humans can reason about the situation and anticipate what might come next ("then, she examined the engine"). SWAG (Situations With Adversarial Generations) is a large-scale dataset for this task of grounded commonsense inference, unifying natural language inference and physically grounded reasoning.

Options:- A: Visual7W  B: VideoNavQA  C: TVQA  D: SWAG
Visual7W