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 train an action segmentation model from weakly-labeled videos.

Keyphrases: action segmentation alignment video

Dataset description:
SoccerDB: Comprises of 171,191 video segments from 346 high-quality soccer games. The database contains 702,096 bounding boxes, 37,709 essential event labels with time boundary and 17,115 highlight annotations for object detection, action recognition, temporal action localization, and highlight detection tasks.
AVA: AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity.
Breakfast: The Breakfast Actions Dataset comprises of 10 actions related to breakfast preparation, performed by 52 different individuals in 18 different kitchens. The dataset is one of the largest fully annotated datasets available.
A2D: A2D (Actor-Action Dataset) is a dataset for simultaneously inferring actors and actions in videos. A2D has seven actor classes (adult, baby, ball, bird, car, cat, and dog) and eight action classes (climb, crawl, eat, fly, jump, roll, run, and walk) not including the no-action class, which we also consider.

Options:- A: SoccerDB  B: AVA  C: Breakfast  D: A2D
Breakfast