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: We present a method for semantic inpainting of natural images, which is an important task

Keyphrases: unsupervised semantic inpainting images paragraph-level

Dataset description:
SNLI-VE: Visual Entailment (VE) consists of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal of a trained VE model is to predict whether the image semantically entails the text.
CelebA: CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age.
ST-VQA: ST-VQA aims to highlight the importance of exploiting high-level semantic information present in images as textual cues in the VQA process.
decaNLP: Natural Language Decathlon Benchmark (decaNLP) is a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. The tasks as cast as question answering over a context.

Options:- A: SNLI-VE  B: CelebA  C: ST-VQA  D: decaNLP
CelebA