Omkar2118/datasets huggingface dataset:
LISA Vehicle Detection

This is a dataset for vehicle detection. It consists of:

* Three color video sequences captured at different times of the day and illumination settings: morning, evening, sunny, cloudy, etc.
* Different driving environments: highway and urban.
* Varying traffic conditions: light to dense traffic

Paper: [A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking](https://doi.org/10.1109/TITS.2010.2040177)

Source: [Vehicle Detection Dataset](http://cvrr.ucsd.edu/LISA/vehicledetection.html)

Image Source: [Vehicle Detection Dataset](http://cvrr.ucsd.edu/LISA/vehicledetection.html)
LLAMAS
Full name: Labeled Lane Markers

The unsupervised Labeled Lane MArkerS dataset (LLAMAS) is a dataset for lane detection and segmentation. It contains over 100,000 annotated images, with annotations of over 100 meters at a resolution of 1276 x 717 pixels. The Unsupervised Llamas dataset was annotated by creating high definition maps for automated driving including lane markers based on Lidar. 

Paper: [Unsupervised Labeled Lane Markers Using Maps](https://doi.org/10.1109/ICCVW.2019.00111)

Source: [Unsupervised Llamas Lane Marker Dataset](https://unsupervised-llamas.com/llamas/)

Image Source: [Unsupervised Llamas Lane Marker Dataset](https://unsupervised-llamas.com/llamas/)
VIsual PERception (VIPER)

VIPER is a benchmark suite for visual perception. The benchmark is based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level and high-level vision tasks, including optical flow, semantic instance segmentation, object detection and tracking, object-level 3D scene layout, and visual odometry. Ground-truth data for all tasks is available for every frame. The data was collected while driving, riding, and walking a total of 184 kilometers in diverse ambient conditions in a realistic virtual world. 

Source: [Playing for Benchmarks](/paper/playing-for-benchmarks)

Image Source: [Playing for Benchmarks](/paper/playing-for-benchmarks)

Introducing paper: Playing for Benchmarks
REC-COCO
Full name: Relations in Captions

Relations in Captions (REC-COCO) is a new dataset that contains associations between caption tokens and bounding boxes in images. REC-COCO is based on the MS-COCO and V-COCO datasets. For each image in V-COCO, we collect their corresponding captions from MS-COCO and automatically align the concept triplet in V-COCO to the tokens in the caption. This requires finding the token for concepts such as PERSON. As a result, REC-COCO contains the captions and the tokens which correspond to each subject and object, as well as the bounding boxes for the subject and object.

Source: [Inferring spatial relations from textual descriptions of images](https://arxiv.org/pdf/2102.00997v1.pdf)
Image Source: [https://arxiv.org/pdf/2102.00997v1.pdf](https://arxiv.org/pdf/2102.00997v1.pdf)

Introducing paper: Inferring spatial relations from textual descriptions of images
TRIPOD
Full name: TuRnIng POint Dataset

TRIPOD contains screenplays and plot synopses with turning point (TP) annotations for 99 movies. Each movie contains:

1. The Wikipedia plot synopsis (extended summary of 35 sentences on average) with sentence-level TP annotations.
2. The screenplay (all dialogue and description parts of the movie) segmented into scenes (selected from the Scriptbase dataset).
3. Gold scene-level TP labels for the screenplays of the test set.
3. The cast information (according to IMDb).

TRIPOD is extended in [Movie Summarization via Sparse Graph Construction](https://arxiv.org/pdf/2012.07536.pdf) with more movies in the test set (122 now in total) and multimodal features extracted from the full-length movie videos. The multimodal version can be found here: https://datashare.ed.ac.uk/handle/10283/3819

Introducing paper: Movie Plot Analysis via Turning Point Identification
CSI Screenplay Summarization Corpus

The dataset contains gold-standard summary labels for 39 "CSI: Crime Scene Investigation" episodes from seasons 1-5. Each episode contains the full-length screenplay and human annotations for its summary. The annotations include:

1. scene-level binary labels denoting whether the scene belongs to the summary of the episode
2. aspect-based labels for the scenes that belong to the summary, i.e., which aspect of the summary the scene addresses (e.g., information about the victim, the crime scene, the perpetrator etc.)
3. sentence-level binary labels denoting the sentences of the screenplay that belong to the summary for 10 episodes of the dataset

Introducing paper: Screenplay Summarization Using Latent Narrative Structure
FPV-O

FPV-O is a multi-subject first-person vision dataset of office activities. Office activities include person-to-person interactions, such as chatting and handshaking, person-to-object interactions, such as using a computer or a whiteboard, as well as generic activities such as walking. The videos in the dataset present a number of challenges that, in addition to intra-class differences and inter-class similarities, include frames with illumination changes, motion blur, and lack of texture. 

Paper: [A First-Person Vision Dataset of Office Activities](https://doi.org/10.1007/978-3-030-20984-1_3)

Source: [A first-person vision dataset of office activities](http://www.eecs.qmul.ac.uk/~andrea/fpvo.html)

Image Source: [A First-Person Vision Dataset of Office Activities](https://doi.org/10.1007/978-3-030-20984-1_3)
MERL Shopping

MERL Shopping is a dataset for training and testing action detection algorithms. The MERL Shopping Dataset consists of 106 videos, each of which is a sequence about 2 minutes long. The videos are from a fixed overhead camera looking down at people shopping in a grocery store setting. Each video contains several instances of the following 5 actions: "Reach To Shelf" (reach hand into shelf), "Retract From Shelf " (retract hand from shelf), "Hand In Shelf" (extended period with hand in the shelf), "Inspect Product" (inspect product while holding it in hand), and "Inspect Shelf" (look at shelf while not touching or reaching for the shelf).

Source: [Action Detection in Videos](https://www.merl.com/demos/merl-shopping-dataset)

Image Source: [Action Detection in Videos](https://www.merl.com/demos/merl-shopping-dataset)

Introducing paper: A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection
ASD
Full name: Annotated Semantic Dataset

The Annotated Semantic Dataset is composed of $11$ videos, divided in $3$ activity categories: Biking; Driving and Walking, according to their amount of semantic information. The classes are: $0p$, which represents the videos with approximately no semantic information; $25p$, for the videos containing relevant semantic information in ∼$25%$ of its frames ; the same ideia for the classes $50p$ and $75p$,
The videos were record using a GoPro Hero 3 camera mounted in a helmet for the Biking and Walking videos and attached to a head strap for the Driving videos.

Introducing paper: Towards Semantic Fast-Forward and Stabilized Egocentric Videos
l2d
Full name: Learning to Dance

This dataset is composed of paired videos of people dancing 3 different music styles: Ballet, Michael Jackson and Salsa.
It contains multimodal data (visual data, temporal-graphs and audio) careful-selected from publicly available videos of dancers performing representative movements of the music style and audio data from the respective styles.

This dataset was used to train and evaluate methodologies for motion generation from audio. 

We split the samples into training and evaluation sets. 

The training set has 2352 samples of movements sequences with length 64. Which 525 are from Ballet style, 966 from Michael Jackson (MJ) and 861 from Salsa.

The evaluation set has 471 samples. 134 from Ballet, 102 from MJ and 235 from Salsa.

Introducing paper: Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio
OccludedPASCAL3D+

The **OccludedPASCAL3D+** is a dataset is designed to evaluate the robustness to occlusion for a number of computer vision tasks, such as object detection, keypoint detection and pose estimation. In the OccludedPASCAL3D+ dataset, we simulate partial occlusion by superimposing objects cropped from the MS-COCO dataset on top of objects from the PASCAL3D+ dataset. We only use ImageNet subset in PASCAL3D+, which has 10812 testing images.

Source: [OccludedPASCAL3D+](https://github.com/Angtian/OccludedPASCAL3D)

Image source: [https://github.com/Angtian/OccludedPASCAL3D](https://github.com/Angtian/OccludedPASCAL3D)

Introducing paper: Robust Object Detection under Occlusion with Context-Aware CompositionalNets
THEODORE
Full name: Learning from THEODORE

Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high- resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset.

Introducing paper: Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning
MHRI dataset
Full name: Multimodal Human-Robot Interaction dataset

The dataset includes recordings from 10 different users teaching the robot different common kitchen objects, that consists of synchronized recordings from three cameras and a microphone mounted on the robot:

    An RGB-d camera covers the user manipulation and interaction with the robot
    An RGB-d camera mounted on the top of the robot provides a top view of the whole scenario
    A HD-RGB camera points to the user head to capture face and expressions
Localized Narratives

We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data. We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available. We provide an extensive analysis of these annotations showing they are diverse, accurate, and efficient to produce. We also demonstrate their utility on the application of controlled image captioning.

Introducing paper: Connecting Vision and Language with Localized Narratives
CE4

Given the difficulty to handle planetary data we provide downloadable files in PNG format from the missions Chang'E-3 and Chang'E-4. In addition to a set of scripts to do the conversion given a different PDS4 Dataset. 

This set of images constitute one of the first available datasets to tackle problems of Computer Vision and Learning in the context of space exploration.

Introducing paper: Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
MICC-SRI

The dataset contains 11,913 frame pairs of urban driving footage with and without moving objects, synthetically generated with the CARLA simulator. All frames are available both as RGB images and semantic segmentations. RGB images are non-photorealistic being rendered by a game engine, while semantic segmentations are similar to a real-world segmentations. The dataset is designed to provide a supervision for semantic road inpainting tasks.

Introducing paper: Semantic Road Layout Understanding by Generative Adversarial Inpainting
KITTI-trajectory-prediction

KITTI is a well established dataset in the computer vision community. It has often been used for trajectory prediction despite not having a well defined split, generating non comparable baselines in different works. This dataset aims at bridging this gap and proposes a well defined split of the KITTI data.
Samples are collected as 6 seconds chunks (2seconds for past and 4 for future) in a sliding window fashion from all trajectories in the dataset, including the egovehicle. There are a total of 8613 top-view trajectories for training and 2907 for testing.
Since top-view maps are not provided by KITTI, semantic labels of static categories obtained with DeepLab-v3+ from all frames are projected in a common top-view map using the Velodyne 3D point cloud and IMU. The resulting maps have a spatial resolution of 0.5 meters and are provided along with the trajectories.

Introducing paper: MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction
Glint360K
Full name: Glint360K

The largest and cleanest face recognition dataset Glint360K, 
which contains **`17,091,657`** images of **`360,232`** individuals, baseline models trained on Glint360K can easily achieve state-of-the-art performance.

Introducing paper: Partial FC: Training 10 Million Identities on a Single Machine
IndicCorp

IndicCorp is a large monolingual corpora with around 9 billion tokens covering 12 of the major Indian languages. It has been developed by discovering and scraping thousands of web sources - primarily news, magazines and books, over a duration of several months.

**Languages covered**: Assamese, Bengali, English, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu

**Corpus Format**: The corpus is a single large text file containing one sentence per line. The publicly released version is randomly shuffled, untokenized and deduplicated. 

**Downloads**

| Language | \# News Articles* | Sentences     | Tokens        | Link     |
| -------- | ----------------- | ------------- | ------------- | -------- |
| as       | 0.60M             | 1.39M   |  32.6M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/as.tar.xz) |
| bn       | 3.83M             | 39.9M | 836M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/bn.tar.xz) |
| en       | 3.49M             | 54.3M | 1.22B | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/en.tar.xz) |
| gu       | 2.63M             | 41.1M | 719M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/gu.tar.xz) |
| hi       | 4.95M             | 63.1M |  1.86B | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/hi.tar.xz) |
| kn       | 3.76M             | 53.3M | 713M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/bn.tar.xz) |
| ml       | 4.75M             | 50.2M |  721M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/ml.tar.xz) |
| mr       | 2.31M             | 34.0M | 551M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/mr.tar.xz) |
| or       | 0.69M             | 6.94M   | 107M   | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/or.tar.xz) |
| pa       | 2.64M             | 29.2M |  773M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/pa.tar.xz) |
| ta       | 4.41M             |  31.5M   |  582M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/ta.tar.xz) |
| te       | 3.98M             | 47.9M   |  674M  | [link](https://storage.googleapis.com/ai4bharat-public-indic-nlp-corpora/indiccorp/te.tar.xz) |

\* Excluding articles obtained from the OSCAR corpus

Introducing paper: IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages
RuFa

RuFa (Ruqaa-Farsi) dataset contains images of text written in one of two Arabic fonts: Ruqaa and Nastaliq (Farsi). The dataset contains 40,000 synthesized image and 516 real one, 40,516 in total. Images are in RGB JPG format at 100×100px. Text in the images has varying number of words, position, size, and opacity.

Real images were extracted from:

1. “The Rules of Arabic Calligraphy” by Hashem Al-Khatat - 1986.

2. “Ottman Fonts” by Muhammad Amin Osmanli Ketbkhana.

The synthetization process is described in detail [in this post](https://mhmoodlan.github.io/blog/arabic-font-classification).

Dataset folder structure:

**/rufa (40,516 images)**

* /real (516 images)

        * /ruqaa (260 images)

        * /farsi   (256 images)

* /synth (40,000 images)

        * /ruqaa (20,000 images)

        * /farsi   (20,000 images)
MERL-RAV
Full name: MERL Reannotation of AFLW with Visibility

The MERL-RAV (MERL Reannotation of AFLW with Visibility) Dataset contains over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. The images were annotated by professional labelers, supervised by researchers at Mitsubishi Electric Research Laboratories (MERL).

Introducing paper: LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Synbols

Synbols is a dataset generator designed for probing the behavior of learning algorithms. By defining the distribution over latent factors one can craft a dataset specifically tailored to answer specific questions about a given algorithm.

Default versions of these datasets are also materialized and can serve as benchmarks.

Introducing paper: Synbols: Probing Learning Algorithms with Synthetic Datasets
C&Z

One of the first datasets (if not the first) to highlight the importance of bias and diversity in the community, which started a revolution afterwards. Introduced in 2014 as integral part of a thesis of Master of Science [1,2] at Carnegie Mellon and City University of Hong Kong. It was later expanded by adding synthetic images generated by a GAN architecture at ETH Zürich (in HDCGAN by Curtó et al. 2017). Being then not only the pioneer of talking about the importance of balanced datasets for learning and vision but also for being the first GAN augmented dataset of faces. 

The original description goes as follows:

A bias-free dataset, containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions. C&Z is enhanced with HDCGAN synthetic images, thus being the first GAN augmented dataset of faces.

Dataset: [https://github.com/curto2/c](https://github.com/curto2/c)

Supplement (with scripts to handle the labels): [https://github.com/curto2/graphics](https://github.com/curto2/graphics)

[1] [https://www.curto.hk/c/decurto.pdf](https://www.curto.hk/c/decurto.pdf)

[2] [https://www.zarza.hk/z/dezarza.pdf](https://www.zarza.hk/z/dezarza.pdf)

Introducing paper: High-Resolution Deep Convolutional Generative Adversarial Networks
GEM
Full name: Generation, Evaluation, and Metrics

Generation, Evaluation, and Metrics (GEM) is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics.

GEM aims to:

- measure NLG progress across 13 datasets spanning many NLG tasks and languages.
- provide an in-depth analysis of data and models presented via data statements and challenge sets.
- develop standards for evaluation of generated text using both automated and human metrics.

It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development by extending existing data or developing datasets for additional languages.

Source: [https://gem-benchmark.com/](https://gem-benchmark.com/)
Image Source: [Gehrmann et al](https://arxiv.org/pdf/2102.01672.pdf)

Introducing paper: The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
ALFWorld

ALFWorld contains interactive TextWorld environments (Côté et. al) that parallel embodied worlds in the ALFRED dataset (Shridhar et. al). The aligned environments allow agents to reason and learn high-level policies in an abstract space before solving embodied tasks through low-level actuation.   

Source: [ALFWorld](https://github.com/alfworld/alfworld)

Introducing paper: ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
HQ-WMCA
Full name: High-Quality Wide Multi-Channel Attack database

The High-Quality Wide Multi-Channel Attack database (HQ-WMCA) database consists of 2904 short multi-modal video recordings of both bona-fide and presentation attacks. There are 555 bonafide presentations from 51 participants and the remaining 2349 are presentation attacks. The data is recorded from several channels including color, depth, thermal, infrared (spectra), and short-wave infrared (spectra).

Introducing paper: Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks
The Best Sarcasm Annotated Dataset in Spanish

### Content

This dataset contains all utterances of two episodes of South Park (Latin American voices) and two episodes of Archer (Spanish voices). The order of the utterances is shuffled. Each utterance has been annotated based on whether it is sarcastic or not. Sarcastic expressions also contain further annotation based on different theories on sarcasm.

This corpus is unique because it is annotated from primarily audiovisual media. It also contains a lot of negative examples of sentences that are meant to be humorous or outrageous, but not sarcastic. This data provides thus a closer to real life benchmark for any sarcasm detection system.

### Cite

I annotated this data for my MA thesis, so please cite it if you use this data.

Hämäläinen, Mika (2016). [Reconocimiento automático del sarcasmo: ¡Esto va a funcionar bien!](https://www.researchgate.net/publication/339182029_Reconocimiento_automatico_del_sarcasmo_Esto_va_a_funcionar_bien). Helsinki: University of Helsinki, Department of Modern Languages.


### Inspiration

- Sarcasm detection
- Prediction of the theoretical categories of sarcasm

Introducing paper: Reconocimiento automático del sarcasmo: ¡Esto va a funcionar bien!
MIRACL-VC1
Full name: MIRACL-VC1

MIRACL-VC1 is a lip-reading dataset including both depth and color images. It can be used for diverse research fields like visual speech recognition, face detection, and biometrics. Fifteen speakers (five men and ten women) positioned in the frustum of an MS Kinect sensor and utter ten times a set of ten words and ten phrases (see the table below). Each instance of the dataset consists of a synchronized sequence of color and depth images (both of 640x480 pixels).  The MIRACL-VC1 dataset contains a total number of 3000 instances.
XD-Violence

XD-Violence is a large-scale audio-visual dataset for violence detection in videos.

Introducing paper: Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
PatentMatch
Full name: PatentMatch

We address the computer-assisted search for prior art by creating a training dataset for supervised machine learning called PatentMatch. It contains pairs of claims from patent applications and semantically corresponding text passages of different degrees from cited patent documents. Each pair has been labeled by technically-skilled patent examiners from the European Patent Office. Accordingly, the label indicates the degree of semantic correspondence (matching), i.e., whether the text passage is prejudicial to the novelty of the claimed invention or not.

Introducing paper: PatentMatch: A Dataset for Matching Patent Claims & Prior Art
A Dataset of Journalists' Interactions with Their Readership

We present a dataset of dialogs in which journalists of The Guardian replied to reader comments and identify the reasons why. Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction.

Introducing paper: A Dataset of Journalists' Interactions with Their Readership: When Should Article Authors Reply to Reader Comments?
Top Comment or Flop Comment?
Full name: Top Comment or Flop Comment? User Engagement in Online News Discussions

This dataset comprises four files of IDs of either strongly or weakly engaging online news comments (please see the paper for details):
"Top comments" are 1) the top 10% comments in the politics section of The Guardian with the largest relative number of *replies* received (3111 samples) and 2) the top 10% comments in the politics section with the largest relative number of *upvotes* received (11081 samples)

"Flop comments" are 1) the flop 10% comments in the politics section of The Guardian with the smallest relative number of *replies* received (3111 samples) and 2) the flop 10% comments in the politics section with the smallest relative number of *upvotes* received (11081 samples)

Introducing paper: Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions
HeartSeg
Full name: HeartSeg

The medaka (Oryzias latipes) and the zebrafish (Danio rerio) are used as a model organism for a variety of subjects in biomedical research. The presented work aims to study the potential of automated ventricular dimension estimation through heart segmentation in medaka. For more on this, it's time for a closer look on our paper and the supplementary materials.

See our paper here: https://www.liebertpub.com/doi/10.1089/zeb.2019.1754

See demonstration of our algorithm and framework on the test set data:
https://youtu.be/i5bX_XbwXq0

The raw data was provided by:
Dr. Jakob Gierten

Affiliated with:
Department of Pediatric Cardiology, University Hospital Heidelberg, Im Neuenheimer Feld 430, 69120 Heidelberg, Germany
Centre for Organismal Studies, Heidelberg University, Im Neuenheimer Feld 230, 69120 Heidelberg, Germany

Contributing

We hope this work sparks additional research in this direction. Either by contributing to this framework, deploying the framework, or reusing the annotated ground truth data.
In any case feel free to reach out and make sure to reference this work.

Schutera, M., Just, S., Gierten, J., Mikut, R., Reischl, M., & Pylatiuk, C. (2019). Machine learning methods for automated quantification of ventricular dimensions. Zebrafish, 16(6), 542-545.
Contact: mark.schutera@kit.edu and pylatiuk@kit.edu
DNS Challenge
Full name: Deep Noise Suppression Challenge

The DNS Challenge at INTERSPEECH 2020 intended to promote collaborative research in single-channel Speech Enhancement aimed to maximize the perceptual quality and intelligibility of the enhanced speech. The challenge evaluated the speech quality using the online subjective evaluation framework ITU-T P.808. The challenge provides large datasets for training noise suppressors.

Source: [Deep Noise Suppression Challenge – INTERSPEECH 2020](https://www.microsoft.com/en-us/research/academic-program/deep-noise-suppression-challenge-interspeech-2020/)

Introducing paper: The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
Interspeech 2021 Deep Noise Suppression Challenge

The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality.

This challenge has two two tracks:

**Track 1: Real-Time Denoising track for wide band scenario**

The noise suppressor must take less than the stride time Ts (in ms) to process a frame of size T (in ms) on an Intel Core i5 quad-core machine clocked at 2.4 GHz or equivalent processor. For example, Ts = T/2 for 50% overlap between frames. The total algorithmic latency allowed including the frame size T, stride time Ts, and any look ahead must be less than or equal to 40ms. For example, for a real-time system that receives 20ms audio chunks, if you use a frame length of 20ms with a stride of 10ms resulting in an algorithmic latency of 30ms, then you satisfy the latency requirements. If you use a frame of size 32ms with a stride of 16ms resulting in an algorithmic latency of 48ms, then your method does not satisfy the latency requirements as the total algorithmic latency exceeds 40ms. If your frame size plus stride T1=T+Ts is less than 40ms, then you can use up to (40-T1) ms future information.

**Track 2: Real-Time Denoising track for full band scenario**

Satisfy Track 1 requirements but at 48 kHz.

Introducing paper: Interspeech 2021 Deep Noise Suppression Challenge
TRN
Full name: Toulouse Road Network

The Toulouse Road Network dataset describes patches of road maps from the city of Toulouse, represented both as spatial graphs G = (A, X) and as grayscale segmentation images. 

The TRN dataset contains 111,034 data points (map tiles), of which: 80,357 are in the training set (around 72.4%), 11,679 are in the validation set (around 10.5%), 18,998 are in the test set (around 17.1%). 
  
Each tile represents a squared region of side 0.001 degrees of latitude and longitude on the map, which corresponds to a square of around 110 meters. The semantic segmentation of each patch is represented as a 64 × 64 grayscale image. 

The dataset is generated starting from publicly available data from OpenStreetMap. More details on the dataset characteristic and generation methods are available in our [blogpost](https://davide-belli.github.io/toulouse-road-network.html).

Source: [https://github.com/davide-belli/toulouse-road-network-dataset](https://github.com/davide-belli/toulouse-road-network-dataset)
Image Source: [https://arxiv.org/pdf/1910.14388.pdf](https://arxiv.org/pdf/1910.14388.pdf)

Introducing paper: Image-Conditioned Graph Generation for Road Network Extraction
WEB-FORUM-52
Full name: WEB-FORUM-52 gold standard

The WEB-FORUM-52 gold standard comprises (i) 13 web forums from the health domain, (ii) 15 forums obtained from a Wikipedia list of popular forums (https://en.wikipedia.org/wiki/List_of_Internet_forums), (iii) 13 forums mentioned on a list of popular German Web forums (https://www.beliebte-foren.de), (iv) nine forums obtained from WPressBlog (https://www.wpressblog.com/free-forum-posting-sites-list/) and (v) two additional forums. For most forums two web pages (from different threads) were used and stored together with gold standard annotations that have been manually created by domain experts and describe the post text, post date, post user and direct URL to the post.

Introducing paper: Harvest -- An Open Source Toolkit for Extracting Posts and Post Metadata from Web Forums
KorQuAD
Full name: The Korean Question Answering Dataset

KorQuAD is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand the distribution of answers and the types of reasoning required to answer the question. This dataset benchmarks the data generating process of SQuAD to meet the standard.

Introducing paper: KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension
MOBIO

The MOBIO database consists of bi-modal (audio and video) data taken from 152 people. The database has a female-male ratio or nearly 1:2 (100 males and 52 females) and was collected from August 2008 until July 2010 in six different sites from five different countries. This led to a diverse bi-modal database with both native and non-native English speakers.

In total 12 sessions were captured for each client: 6 sessions for Phase I and 6 sessions for Phase II. The Phase I data consists of 21 questions with the question types ranging from: Short Response Questions, Short Response Free Speech, Set Speech, and Free Speech. The Phase II data consists of 11 questions with the question types ranging from:  Short Response Questions, Set Speech, and Free Speech. A more detailed description of the questions asked of the clients is provided below.

The database was recorded using two mobile devices: a mobile phone and a laptop computer. The mobile phone used to capture the database was a NOKIA N93i mobile while the laptop computer was a standard 2008 MacBook. The laptop was only used to capture part of the first session, this first session consists of data captured on both the laptop and the mobile phone.
FRLL-Morphs
Full name: Face Research London Lab Morphs

FRLL-Morphs is a dataset of morphed faces based on images selected from the publicly available Face Research London Lab dataset [1].
 
We created the database by selecting similar looking pairs of people, and made 4 types of morphs for each pair using the following morphing tools: OpenCV [2], FaceMorpher [3], StyleGAN 2 [3], WebMorpher [4].

* [1] https://figshare.com/articles/dataset/Face_Research_Lab_London_Set/5047666
* [2] https://www.learnopencv.com/face-morph-using-opencv-cpp-python
* [3] https://github.com/yaopang/FaceMorpher/tree/master/facemorpher
* [4] https://github.com/NVlabs/stylegan2

Introducing paper: Are GAN-based Morphs Threatening Face Recognition?
VisualMRC
Full name: VisualMRC: Machine Reading Comprehension on Document Images

VisualMRC is a visual machine reading comprehension dataset that proposes a task: given a question and a document image, a model produces an abstractive answer.

You can find more details, analyses, and baseline results in the paper, 
VisualMRC: Machine Reading Comprehension on Document Images, AAAI 2021.


Statistics:
10,197 images
30,562 QA pairs
10.53 average question tokens (tokenizing with NLTK tokenizer)
9.53 average answer tokens (tokenizing wit NLTK tokenizer)
151.46 average OCR tokens (tokenizing with NLTK tokenizer)

Introducing paper: VisualMRC: Machine Reading Comprehension on Document Images
FERET-Morphs

FERET-Morphs is a dataset of morphed faces selected from the publicly available FERET dataset [1].

We created the database by selecting similar looking pairs of people, and made 3 types of morphs for each pair using the following morphing tools: OpenCV [2], FaceMorpher [3], StyleGAN 2 [3].

* [1] https://www.nist.gov/itl/products-and-services/color-feret-database
* [2] https://www.learnopencv.com/face-morph-using-opencv-cpp-python
* [3] https://github.com/yaopang/FaceMorpher/tree/master/facemorpher
* [4] https://github.com/NVlabs/stylegan2

Introducing paper: Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
FRGC-Morphs

FRGC-Morphs is a dataset of morphed faces selected from the publicly available FRGC dataset [1].

We created the database by selecting similar looking pairs of people, and made 3 types of morphs for each pair using the following morphing tools: OpenCV [2], FaceMorpher [3], StyleGAN 2 [3].

* [1] https://www.nist.gov/programs-projects/face-recognition-grand-challenge-frgc
* [2] https://www.learnopencv.com/face-morph-using-opencv-cpp-python
* [3] https://github.com/yaopang/FaceMorpher/tree/master/facemorpher
* [4] https://github.com/NVlabs/stylegan2

Introducing paper: Are GAN-based Morphs Threatening Face Recognition?
NISP- A Multi-lingual Multi-accent Dataset for Speaker Profiling

We announce the release of a new multilingual speaker dataset called NITK-IISc Multilingual Multi-accent Speaker Profiling(NISP) dataset. The dataset contains speech in six different languages -- five Indian languages along with Indian English. The dataset contains speech data from 345 bilingual speakers in India. Each speaker has contributed about 4-5 minutes of data that includes recordings in both English and their mother tongue. The transcript for the text is provided in UTF-8 format. For every speaker, the dataset contains speaker meta-data such as L1, native place, medium of instruction, current residing place etc. In addition the dataset also contains physical parameter information of the speakers such as age, height, shoulder size and weight. We hope that the dataset is useful for a diverse set of research activities including multilingual speaker recognition, language and accent recognition, automatic speech recognition etc.

Introducing paper: NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling
NinaPro DB2
Full name: DB2 - 40 Intact Subjects - Delsys Trigno electrodes

The second Ninapro database includes 40 intact subjects and it is thoroughly described in the paper: "Manfredo Atzori, Arjan Gijsberts, Claudio Castellini, Barbara Caputo, Anne-Gabrielle Mittaz Hager, Simone Elsig, Giorgio Giatsidis, Franco Bassetto & Henning Müller. Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Scientific Data, 2014" (http://www.nature.com/articles/sdata201453).
Please, cite this paper for any work related to the Ninapro database.
Please, use also the paper by Gijsberts et al., 2014 (http://publications.hevs.ch/index.php/publications/show/1629) for more information about the database.
BuzzFeed-Webis Fake News Corpus 2016

The BuzzFeed-Webis Fake News Corpus 16 comprises the output of 9 publishers in a week close to the US elections. Among the selected publishers are 6 prolific hyperpartisan ones (three left-wing and three right-wing), and three mainstream publishers (see Table 1). All publishers earned Facebook’s blue checkmark, indicating authenticity and an elevated status within the network. For seven weekdays (September 19 to 23 and September 26 and 27), every post and linked news article of the 9 publishers was fact-checked by professional journalists at BuzzFeed. In total, 1,627 articles were checked, 826 mainstream, 256 left-wing and 545 right-wing. The imbalance between categories results from differing publication frequencies.

Introducing paper: A Stylometric Inquiry into Hyperpartisan and Fake News
POLIT-FALSE-n-LEGIT NEWS DB 2016-2017

The LiT.RL POLIT-FALSE-n-LEGIT NEWS DB 2016-2017 contains a total of 274 news articles about U.S. Politics, content-matched in pairs of legitimate and falsified news. The database is free and released under an open license for educational and research purposes.
GQN rooms-ring-camera

GQN rooms-ring-camera consist of scenes of a variable number of random objects captured in a square room of size 7x7 units. Wall textures, floor textures as well as the shapes of the objects are randomly chosen within a fixed pool of discrete options. There are 5 possible wall textures (red, green, cerise, orange, yellow), 3 possible floor textures (yellow, white, blue) and 7 possible object shapes (box, sphere, cylinder, capsule, cone, icosahedron and triangle). Each scene contains 1, 2 or 3 objects. In this simplified version of the dataset, the camera only moves on a fixed ring and always faces the center of the room.
ISOT Fake News Dataset

The ISOT Fake News dataset is a compilation of several thousands fake news and truthful articles, obtained from different legitimate news sites and sites flagged as unreliable by Politifact.com.
ObjectsRoom

The **ObjectsRoom** dataset is based on the MuJoCo environment used by the Generative Query Network [4] and is a multi-object extension of the 3d-shapes dataset. The training set contains 1M scenes with up to three objects. We also provide ~1K test examples for the following variants:

2.1 Empty room: scenes consist of the sky, walls, and floor only.

2.2 Six objects: exactly 6 objects are visible in each image.

2.3 Identical color: 4-6 objects are placed in the room and have an identical, randomly sampled color.

Datapoints consist of an image and fixed number of masks. The first four masks correspond to the sky, floor, and two halves of the wall respectively. The remaining masks correspond to the foreground objects.

Source: [Objects Room](https://github.com/deepmind/multi_object_datasets)