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: I want to implement a new zero-shot learning algorithm.

Keyphrases: zero-shot learning images

Dataset description:
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
KILT: KILT (Knowledge Intensive Language Tasks) is a benchmark consisting of 11 datasets representing 5 types of tasks:   Fact-checking (FEVER), Entity linking (AIDA CoNLL-YAGO, WNED-WIKI, WNED-CWEB), Slot filling (T-Rex, Zero Shot RE), Open domain QA (Natural Questions, HotpotQA, TriviaQA, ELI5), Dialog generation (Wizard of Wikipedia). All these datasets have been grounded in a single pre-processed wikipedia snapshot, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning.
LAD: LAD (Large-scale Attribute Dataset) has 78,017 images of 5 super-classes and 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets (AwA, CUB, aP/aY and SUN).
Tasty Videos: A collection of 2511 recipes for zero-shot learning, recognition and anticipation.

Options:- A: ImageNet  B: KILT  C: LAD  D: Tasty Videos
ImageNet