Omkar2118/datasets huggingface dataset:
HEV-I
Full name: Honda Egocentric View-Intersection Dataset

Honda Egocentric View-Intersection Dataset (HEV-I) is introduced to enable research on traffic participants interaction modelling, future object localization, as well as learning driver action in challenging driving scenarios. The dataset includes 230 video clips of real human driving in different intersections from the San Francisco Bay Area, collected using an instrumented vehicle equipped with different sensors including cameras, GPS/IMU, and vehicle states signals.

Introducing paper: Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Win-Fail Action Understanding
Full name: Win-Fail Action Understanding

First  of  its  kind paired win-fail action understanding dataset with samples from  the  following  domains:  “General  Stunts,”  “Internet Wins-Fails,”  “Trick  Shots,”  &  “Party  Games.” The task is to identify successful and failed attempts at various activities. Unlike existing  action  recognition  datasets,  intra-class  variation is  high  making  the  task  challenging,  yet  feasible.

Introducing paper: Win-Fail Action Recognition
Multimodal PISA
Full name: Multimodal Piano Skills Assessment

Dataset for multimodal skills assessment focusing on assessing piano player’s skill level. Annotations include player's skills level, and song difficulty level. Bounding box annotations around pianists' hands are also provided.

Introducing paper: Piano Skills Assessment
RepLab 2013

RepLab 2013 dataset uses Twitter data in English and Spanish (more than 142,000 tweets). The balance between both languages depends on the availability of data for each of the entities included in the dataset. The corpus consists of a collection of tweets referring to a selected set of 61 entities from four domains: automotive, banking, universities and music/artists. The domain selection was done to offer a variety of scenarios for reputation studies.

Crawling was performed during the period from the 1st June 2012 till the 31st Dec 2012 using the entity’s canonical name as query. For each entity, at least 2,200 tweets are collected: at least 700 tweets at the beginning of the timeline are used as training set, and at least 1,500 last tweets are reserved for the test set. The corpus also comprises additional background tweets for each entity (up to 50,000 tweets, with a large variability across entities). This distribution was set in this way to obtain a temporal separation (ideally of several months) between the training and test data.

Note that the final amount of available tweets in these sets may be lower, since some posts may have been deleted by the users: in order to respect Twitter’s terms of service, we do not provide the contents of the tweets. The tweet identifiers can be used to retrieve the texts of the posts. We provide a download tool that is similarly to the mechanism used in the TREC Microblog Track in 2011 and 2012.

For more information, please refer to the [RepLab 2013 Overview's paper](https://dl.acm.org/doi/10.1007/978-3-642-40802-1_31).
LSARS
Full name: Large Scale Abstractive multi-Review Summarization

In an active e-commerce environment, customers process a large number of reviews when deciding on whether to buy a product or not. Abstractive Multi-Review Summarization aims to assist users to efficiently consume the reviews that are the most relevant to them. We propose the first large-scale abstractive multi-review summarization dataset that leverages more than 17.9 billion raw reviews and uses novel aspect-alignment techniques based on aspect annotations. Furthermore, we demonstrate that one can generate higher-quality review summaries by using a novel aspect-alignment-based model. Results from both automatic and human evaluation show that the proposed dataset plus the innovative aspect-alignment model can generate high-quality and trustful review summaries.

Paper: [Large Scale Abstractive Multi-Review Summarization (LSARS) via Aspect Alignment](https://dl.acm.org/doi/abs/10.1145/3397271.3401439)
HumAID
Full name: Human-Annotated Disaster Incidents Data

Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters. Despite its significantly large volume, social media content is often too noisy for direct use in any application. Therefore, it is important to filter, categorize, and concisely summarize the available content to facilitate effective consumption and decision-making. To address such issues automatic classification systems have been developed using supervised modeling approaches, thanks to the earlier efforts on creating labeled datasets. However, existing datasets are limited in different aspects (e.g., size, contains duplicates) and less suitable to support more advanced and data-hungry deep learning models. 

HumAID is a large-scale dataset for crisis informatics research with ~77K human-labeled tweets, sampled from a pool of ~24 million tweets across 19 disaster events that happened between 2016 and 2019. The annotations in the provided datasets consists of following humanitarian categories. The dataset consists only english tweets and it is the largest dataset for crisis informatics so far.

Humanitarian categories:
* Caution and advice
* Displaced people and evacuations
* Don't know can't judge
* Infrastructure and utility damage
* Injured or dead people
* Missing or found people
* Not humanitarian
* Other relevant information
* Requests or urgent needs
* Rescue volunteering or donation effort
* Sympathy and support

Introducing paper: HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
UAV-Human

UAV-Human is a large dataset for human behavior understanding with UAVs. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. The dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. This dataset can be used for UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition.

Introducing paper: UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles