Omkar2118/datasets huggingface dataset:
EMOTIC
Full name: EMOTIons in Context

The EMOTIC dataset, named after EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions. The images are annotated with an extended list of 26 emotion categories combined with the three common continuous dimensions Valence, Arousal and Dominance.

Source: [Context Based Emotion Recognition using EMOTIC Dataset](https://arxiv.org/abs/2003.13401)

Introducing paper: Context Based Emotion Recognition using EMOTIC Dataset
CARER
Full name: Contextualized Affect Representations for Emotion Recognition

CARER is an emotion dataset collected through noisy labels, annotated via distant supervision as in (Go et al., 2009). 

The subset of data provided here corresponds to the six emotions variant described in the paper. The six emotions are anger, fear, joy, love, sadness, and surprise.

Source: [CARER: Contextualized Affect Representations for Emotion Recognition](/paper/carer-contextualized-affect-representations)

Introducing paper: CARER: Contextualized Affect Representations for Emotion Recognition
EMU
Full name: Edited Media Understanding

48k question-answer pairs written in rich natural language.

Source: [Edited Media Understanding: Reasoning About Implications of Manipulated Images](/paper/edited-media-understanding-reasoning-about)

Introducing paper: Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
EndoSLAM
Full name: Endoscopic SLAM dataset

The endoscopic SLAM dataset (**EndoSLAM**) is a dataset for depth estimation approach for endoscopic videos. It consists of both ex-vivo and synthetically generated data. The ex-vivo part of the dataset includes standard as well as capsule endoscopy recordings. The dataset is divided into 35 sub-datasets. Specifically, 18, 5 and 12 sub-datasets exist for colon, small intestine and stomach respectively.

Source: [https://github.com/CapsuleEndoscope/EndoSLAM](https://github.com/CapsuleEndoscope/EndoSLAM)
Image Source: [https://github.com/CapsuleEndoscope/EndoSLAM](https://github.com/CapsuleEndoscope/EndoSLAM)

Introducing paper: EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
ENT-DESC

ENT-DESC involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions.

Source: [ENT-DESC: Entity Description Generation by Exploring Knowledge Graph](/paper/knowledge-graph-empowered-entity-description)

Introducing paper: ENT-DESC: Entity Description Generation by Exploring Knowledge Graph
EORSSD
Full name: Extended Optical Remote Sensing Saliency Detection

The **Extended Optical Remote Sensing Saliency Detection** (**EORSSD**) dataset is an extension of the ORSSD dataset. This new dataset is larger and more varied than the original. It contains 2,000 images and corresponding pixel-wise ground truth, which includes many semantically meaningful but challenging images.

Source: [https://github.com/rmcong/EORSSD-dataset](https://github.com/rmcong/EORSSD-dataset)
Image Source: [Zhang et al](https://arxiv.org/pdf/2011.13144v1.pdf)

Introducing paper: Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
EPIE

Corpus containing 25206 sentences labelled with lexical instances of 717 idiomatic expressions. These spans also cover literal usages for the given set of idiomatic expressions. 

Source: [EPIE Dataset: A Corpus For Possible Idiomatic Expressions](/paper/epie-dataset-a-corpus-for-possible-idiomatic)

Introducing paper: EPIE Dataset: A Corpus For Possible Idiomatic Expressions
ERA
Full name: Event Recognition in Aerial videos

Consists of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales.

Source: [ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos](/paper/era-a-dataset-and-deep-learning-benchmark-for)

Introducing paper: ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
esXNLI

**esXNLI** is a bilingual NLI dataset. It comprises 2,490 examples from 5 different genres that were originally annotated in Spanish, and translated into English by professional translators. It serves as a counterpoint to XNLI, which was originally annotated in English and translated into 14 other languages, including Spanish. The dataset was conceived to be used in conjunction with the XNLI development set to analyse the effect of translation in cross-lingual transfer learning.

Source: [https://github.com/artetxem/esxnli](https://github.com/artetxem/esxnli)

Introducing paper: Translation Artifacts in Cross-lingual Transfer Learning
ETHICS

A new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality.

Source: [Aligning AI With Shared Human Values](/paper/aligning-ai-with-shared-human-values)

Introducing paper: Aligning AI With Shared Human Values