Omkar2118/datasets huggingface dataset:
Custom Spatio-Temporal Action Video Dataset

This spatio-temporal actions dataset for video understanding consists of 4 parts: original videos, cropped videos, video frames, and annotation files. This dataset uses a proposed new multi-person annotation method of spatio-temporal actions. First, we use ffmpeg to crop the videos and frame the videos; then use yolov5 to detect human in the video frame, and then use deep sort to detect the ID of the human in the video frame. By processing the detection results of yolov5 and deep sort, we can get the annotation file of the spatio-temporal action dataset to complete the work of customizing the spatio-temporal action dataset.

Introducing paper: A Multi-Person Video Dataset Annotation Method of Spatio-Temporally Actions
3MASSIV

A multilingual, multimodal and multi-aspect, expertly-annotated dataset of diverse short videos extracted from short-video social media platform - Moj. 3MASSIV comprises of 50k short videos (~20 seconds average duration) and 100K unlabeled videos in 11 different languages and captures popular short video trends like pranks, fails, romance, comedy expressed via unique audio-visual formats like self-shot videos, reaction videos, lip-synching, self-sung songs, etc.

Introducing paper: 3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social Media Short Videos
PeerSum

**PeerSum** is a new MDS dataset using peer reviews of scientific publications. The dataset differs from the existing MDS datasets in that summaries (i.e., the meta-reviews) are highly abstractive and they are real summaries of the source documents. 

In PeerSum, we have reviews (with scores), comments and responses as the source documents and the meta-review (with an acceptance outcome) as the ground truth summary. Each sample of this dataset contains a summary, corresponding source documents and also other complementary information (e.g., review scores) for one paper. The second version of PeerSum (peersum_v2) has 16,308 samples, while there are 10,862 samples in the first version.

The dataset is stored in the json format. For each sample, details are based on following keys with explanation:

* paper_id: unique id for each sample
* title: the title of the corresponding paper
* abstract: paper abstract
* score: final score of this paper (if there is not a final, it will be an average of review scores)
* acceptance: acceptance of the paper (e.g., accept, reject or spotlight)
* meta_review: meta-review of the paper and this is treated as the summary
* reviews: [review_id, writer, content (rating, confidence, comment), replyto]   review_id and replyto are for the conversation structure
* label: train, val, test (8/1/1)

For each review (i.e., official review, public comment, or author/reviewer response):
* review_id: unique id of each review
* writer: official_reviewer, public, author
* content: (rating, confidence, comment)
* replyto: connect to a review (review_id and replyto are for the conversation structure)

Introducing paper: PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization