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: I want to build a few-shot temporal activity detection model.

Keyphrases: temporal activity detection video

Dataset description:
SYNTHIA-AL: Specially designed to evaluate active learning for video object detection in road scenes.
Composable activities dataset: The Composable activities dataset consists of 693 videos that contain activities in 16 classes performed by 14 actors. Each activity is composed of 3 to 11 atomic actions.
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).
FSOD: Few-Shot Object Detection Dataset (FSOD) is a high-diverse dataset specifically designed for few-shot object detection and intrinsically designed to evaluate thegenerality of a model on novel categories.
PASCAL-5i: PASCAL-5i is a dataset used to evaluate few-shot segmentation. The dataset is sub-divided into 4 folds each containing 5 classes.
ActivityNet: The ActivityNet dataset contains 200 different types of activities and a total of 849 hours of videos collected from YouTube. ActivityNet is the largest benchmark for temporal activity detection to date in terms of both the number of activity categories and number of videos, making the task particularly challenging.
FC100: The FC100 dataset (Fewshot-CIFAR100) is a newly split dataset based on CIFAR-100 for few-shot learning. It contains 20 high-level categories which are divided into 12, 4, 4 categories for training, validation and test.

Options:- A: SYNTHIA-AL  B: Composable activities dataset  C: UCF101  D: FSOD  E: PASCAL-5i  F: ActivityNet  G: FC100
["UCF101", "ActivityNet"]