You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: Temporal embedding learning for video frames.

Keyphrases: temporal embedding learning video

Dataset description:
TUM Kitchen: The TUM Kitchen dataset is an action recognition dataset that contains 20 video sequences captured by 4 cameras with overlapping views. The camera network captures the scene from four viewpoints with 25 fps, and every RGB frame is of the resolution 384×288 by pixels.
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.
Cityscapes-VPS: Cityscapes-VPS is a video extension of the Cityscapes validation split. It provides 2500-frame panoptic labels that temporally extend the 500 Cityscapes image-panoptic labels.
UCF101: UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports).
JHMDB: JHMDB is an action recognition dataset that consists of 960 video sequences belonging to 21 actions. It is a subset of the larger HMDB51 dataset collected from digitized movies and YouTube videos.

Options:- A: TUM Kitchen  B: ImageNet  C: Cityscapes-VPS  D: UCF101  E: JHMDB
["ImageNet", "UCF101"]