Omkar2118/datasets huggingface dataset:
WIKIOG

WIKIOG is a public collection which consists of over 1.75 million document-outline pairs for research on the OG task. 

Source: [Outline Generation: Understanding the Inherent Content Structure of Documents](https://arxiv.org/pdf/1905.10039)

Introducing paper: Outline Generation: Understanding the Inherent Content Structure of Documents
SemanticUSL

SemanticUSL is a dataset for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. 

Source: [LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2003.01174)
Image Source: [https://unmannedlab.github.io/semanticusl](https://unmannedlab.github.io/semanticusl)

Introducing paper: LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation
Jericho

Jericho is a learning environment for man-made Interactive Fiction (IF) games.

Source: [Interactive Fiction Games: A Colossal Adventure](https://arxiv.org/pdf/1909.05398.pdf)

Introducing paper: Interactive Fiction Games: A Colossal Adventure
3D-FRONT
Full name: 3D-FRONT

**3D-FRONT** (3D Furnished Rooms with layOuts and semaNTics) is large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, the dataset is freely available to the academic community and beyond. 

3D-FRONT contains 18,797 rooms diversely furnished by 3D objects. In addition, the 7,302 furniture objects all come with high-quality textures. While the floorplans and layout designs are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system to attain consistent styles as expert designs.
JParaCrawl

JParaCrawl is a parallel corpus for English-Japanese, for which the amount of publicly available parallel corpora is still limited. The parallel corpus was constructed by broadly crawling the web and automatically aligning parallel sentences. The corpus amassed over 8.7 million sentence pairs.

Source: [JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus](https://arxiv.org/pdf/1911.10668)

Introducing paper: JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus
3ThreeDWorld
Full name: 3ThreeDWorld

**TDW** is a 3D virtual world simulation platform, utilizing state-of-the-art video game engine technology. A TDW simulation consists of two components: a) the Build, a compiled executable running on the Unity3D Engine, which is responsible for image rendering, audio synthesis and physics simulations; and b) the Controller, an external Python interface to communicate with the build.

Introducing paper: ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
MUSIC
Full name: Multi-Spectral Imaging via Computed Tomography

The Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset is a two-part (2D- and 3D spectral) open access dataset for advanced image analysis of spectral radiographic (x-ray) scans, their tomographic reconstruction and the detection of specific materials within such scans. The scans operate at a photon energy range of around 20 keV up to 160 keV.

The dataset includes — for 2D- as well as 3D spectral data — the corrected (e.g. calibrated) radiographic projections, their tomographic reconstructions (based on 37 projections of 256 detector pixels into a 100×100 pixel CT image per slice) and the corresponding set of segmentation variants.

Source: [Multi-Spectral Imaging via Computed Tomography (MUSIC)](http://easi-cil.compute.dtu.dk/index.php/datasets/music/)

Introducing paper: Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiation
MuMu

MuMu is a new dataset of more than 31k albums classified into 250 genre classes.

Source: [Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features](https://arxiv.org/pdf/1707.04916)

Introducing paper: Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features
FSVQA
Full name: Full-Sentence Visual Question Answering

Full-Sentence Visual Question Answering (FSVQA) dataset, consisting of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to original VQA dataset and captions in the MS COCO dataset.

Source: [The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)](https://arxiv.org/abs/1609.06657)

Introducing paper: The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)