Omkar2118/datasets huggingface dataset:
The China Physiological Signal Challenge 2018

The China Physiological Signal Challenge 2018 aims to encourage the development of algorithms to identify the rhythm/morphology abnormalities from 12-lead ECGs. The data used in CPSC 2018 include one normal ECG type and eight abnormal types.

Source: [An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection](/paper/an-open-access-database-for-evaluating-the)

Introducing paper: An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection
University-1652

Contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. University-1652 is a drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. 

Source: [University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization](/paper/university-1652-a-multi-view-multi-source)

Introducing paper: University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
MSVD-QA

The MSVD-QA dataset is a Video Question Answering (VideoQA) dataset. It is based on the existing Microsoft Research Video Description (MSVD) dataset, which consists of about 120K sentences describing more than 2,000 video snippets. In the MSVD-QA dataset, Question-Answer (QA) pairs are generated from these descriptions. The dataset is mainly used in video captioning experiments but due to its large data size, it is also used for VideoQA. It contains 1970 video clips and approximately 50.5K QA pairs.
MSRVTT-QA

The **MSR-VTT-QA** dataset is a benchmark for the task of Visual Question Answering (VQA) on the MSR-VTT (Microsoft Research Video to Text) dataset. The **MSR-VTT-QA** benchmark is used to evaluate models on their ability to answer questions based on these videos. It's part of the tasks that this dataset is used for, along with Video Retrieval, Video Captioning, Zero-Shot Video Question Answering, Zero-Shot Video Retrieval, and Text-to-Video Generation.
FQuAD
Full name: French Question Answering Dataset

A French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version.

Source: [FQuAD: French Question Answering Dataset](/paper/fquad-french-question-answering-dataset)

Introducing paper: FQuAD: French Question Answering Dataset
HARD
Full name: Hotel Arabic-Reviews Dataset

The Hotel Arabic-Reviews Dataset (HARD) contains 93700 hotel reviews in Arabic language. The hotel reviews were collected from Booking.com website during June/July 2016. The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.

Source: [HARD](https://github.com/elnagara/HARD-Arabic-Dataset)
3DFAW

**3DFAW** contains 23k images with 66 3D face keypoint annotations.

Source: [Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild](https://arxiv.org/abs/1911.11130)
Image Source: [http://mhug.disi.unitn.it/workshop/3dfaw/](http://mhug.disi.unitn.it/workshop/3dfaw/)

Introducing paper: The First 3D Face Alignment in the Wild (3DFAW) Challenge