Omkar2118/datasets huggingface dataset:
CITR & DUT
Full name: CITR dataset and DUT dataset

Consists of two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. 

CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI.

DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration.

Source: [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](/paper/top-view-trajectories-a-pedestrian-dataset-of)

Introducing paper: Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
JNC
Full name: Japanese News Corpus

The JNC data provides common supervision data for headline generation.

Source: [A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation](/paper/a-large-scale-multi-length-headline-corpus)

Introducing paper: A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation
JHU-CROWD++

JHU-CROWD++ is A large-scale unconstrained crowd counting dataset with 4,372 images and 1.51 million annotations. This dataset is collected under a variety of diverse scenarios and environmental conditions. In addition, the dataset provides comparatively richer set of annotations like dots, approximate bounding boxes, blur levels, etc.

Introducing paper: JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
Action Recognition in the Dark
Full name: ARID

ARID is a dataset for action recognition in dark videos. It consists of over 3,780 video clips with 11 action categories.

Source: [ARID: A New Dataset for Recognizing Action in the Dark](/paper/arid-a-new-dataset-for-recognizing-action-in)

Introducing paper: ARID: A New Dataset for Recognizing Action in the Dark
Kuzushiji-Kanji

Kuzushiji-Kanji is an imbalanced dataset of total 3832 Kanji characters (64x64 grayscale, 140,426 images), ranging from 1,766 examples to only a single example per class. Kuzushiji is a Japanese cursive writing style.

Source: [Deep Learning for Classical Japanese Literature](/paper/deep-learning-for-classical-japanese)
Image Source: [Kuzushiji-MNIST](https://github.com/rois-codh/kmnist)

Introducing paper: Deep Learning for Classical Japanese Literature
UCO-LAEO

A dataset for building models that detect people Looking At Each Other (LAEO) in video sequences.

Source: [LAEO-Net: revisiting people Looking At Each Other in videos](/paper/laeo-net-revisiting-people-looking-at-each-1)

Introducing paper: LAEO-Net: revisiting people Looking At Each Other in videos
Skeletics 152

A curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset.

Source: [Quo Vadis, Skeleton Action Recognition ?](/paper/quo-vadis-skeleton-action-recognition)

Introducing paper: Quo Vadis, Skeleton Action Recognition ?
Spot the Difference Corpus

Spot the Difference Corpus is a corpus of task-oriented spontaneous dialogues which contains 54 interactions between pairs of subjects interacting to find differences in two very similar scenes. The corpus includes rich transcriptions, annotations, audio and video.

Source: [The Spot the Difference corpus: a multi-modal corpus of spontaneous task oriented spoken interactions](https://arxiv.org/pdf/1805.05091.pdf)

Introducing paper: The Spot the Difference corpus: a multi-modal corpus of spontaneous task oriented spoken interactions
CCPE-M
Full name: Coached Conversational Preference Elicitation dataset for Movies

A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language.

The corpus was constructed from dialogues between two paid crowd-workers using a Wizard-of-Oz methodology. One worker plays the role of an "assistant", while the other plays the role of a "user". The "assistant" is tasked with eliciting the "user" preferences about movies following a Coached Conversational Preference Elicitation (CCPE) methodology. In particular, the assistant is required to ask questions designed so as to minimize the bias in the terminology the "user" employs to convey his or her preferences, and obtain these in as natural language as possible. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities.

Source: [CCPE-M: Coached Conversational Preference Elicitation dataset for Movies](https://github.com/google-research-datasets/ccpe)

Introducing paper: Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences
COCO-CN

COCO-CN is a bilingual image description dataset enriching MS-COCO with manually written Chinese sentences and tags. The new dataset can be used for multiple tasks including image tagging, captioning and retrieval, all in a cross-lingual setting.

Source: [COCO-CN](https://github.com/li-xirong/coco-cn)

Introducing paper: COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval
T2 Guiding

T2 Guiding is a dataset of 1000 images, each with six image labels. The images are from the Open Images Dataset (OID) and the dataset includes 2 sets of machine-generated labels for these images.

* Object labels: Three random object labels generated by a FRCNN model trained on Visual Genome.
* Image labels: Three random image labels obtained from Google Cloud Vision API.

Source: [T2-Guiding](https://github.com/google-research-datasets/T2-Guiding)

Introducing paper: Understanding Guided Image Captioning Performance across Domains
FarsBase-KBP

FarsBase-KBP contains 22015 sentences, in which the entities and relation types are linked to the FarsBase ontology. This gold dataset can be reused for benchmarking KBP systems in the Persian language.

Source: [](https://arxiv.org/pdf/2005.01879.pdf)

Introducing paper: FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph
ReQA
Full name: Retrieval Question-Answering

Retrieval Question-Answering (ReQA) benchmark tests a model’s ability to retrieve relevant answers efficiently from a large set of documents.

Source: [ReQA: An Evaluation for End-to-End Answer Retrieval Models](https://arxiv.org/pdf/1907.04780.pdf)

Introducing paper: ReQA: An Evaluation for End-to-End Answer Retrieval Models
ROSE
Full name: Retinal OCTA SEgmentation dataset

Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level.

Source: [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://arxiv.org/pdf/2007.05201.pdf)
Image Source: [https://imed.nimte.ac.cn/dataofrose.html](https://imed.nimte.ac.cn/dataofrose.html)

Introducing paper: ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
UDIVA

UDIVA is a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self and peer-reported personality, internal state, and relationship profiling from participants. 

Source: [Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset](https://arxiv.org/pdf/2012.14259.pdf)

Introducing paper: Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset
MuST-Cinema

MuST-Cinema is a Multilingual Speech-to-Subtitles corpus ideal for building subtitle-oriented machine and speech translation systems.
It comprises audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations.

MuST-Cinema was built by annotating MuST-C with subtitle breaks based on the original subtitle files. Special symbols have been inserted in the aligned sentences to mark subtitle breaks as follows:

- <eob>: block break (breaks between subtitle blocks)
- <eol>: line breaks (breaks between lines inside the same subtitle block)

Source: [MuST-Cinema](https://ict.fbk.eu/must-cinema/)

Introducing paper: MuST-Cinema: a Speech-to-Subtitles corpus
LIV360SV
Full name: Liverpool 360 degree Street View

The dataset contains 26,645, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th -- 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband).

Source: [A deep learning approach to identify unhealthy advertisements in street view images](https://arxiv.org/pdf/2007.04611)

Introducing paper: A deep learning approach to identify unhealthy advertisements in street view images
i3-video
Full name: is-it-instructional-video

The i3-video dataset contains "is-it-instructional" annotations for 6.4k videos from [Youtube-8M](https://paperswithcode.com/dataset/youtube-8m). The videos are considered to be instructional if they focus on real-world human actions accompanied by procedural language that explains what’s happening on screen in reasonable details.

Source: [i3-video](https://github.com/google-research-datasets/i3-video)

Introducing paper: Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube
CLaRO

CLaRO is a new dataset of 234 Competency Questions that had been processed automatically into 106 patterns. The coverage of CLaRO, with its 93 main templates and 41 linguistic variants, is about 90% for unseen questions.

Source: [CLaRO: a Data-driven CNL for Specifying Competency Questions](https://arxiv.org/pdf/1907.07378)

Introducing paper: CLaRO: a Data-driven CNL for Specifying Competency Questions
ImagiFilter

ImagiFilter focusses on photographic and/or natural images, a very common use-case in computer vision research. Annotations for coarse prediction are provided, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where the non-photographic class is broken down into five classes: maps, drawings, graphs, icons, and sketches.

Source: [ImagiFilter: A resource to enable the semi-automatic mining of images at scale](https://arxiv.org/pdf/2008.09152)

Introducing paper: ImagiFilter: A resource to enable the semi-automatic mining of images at scale
CROSS
Full name: Cross-Reference Omnidirectional Stitching IQA

Cross-Reference Omnidirectional Stitching IQA is a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦ , 180◦ and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions.

Source: [Image Quality Assessment for Omnidirectional Cross-reference Stitching](https://arxiv.org/pdf/1904.04960.pdf)

Introducing paper: Image Quality Assessment for Omnidirectional Cross-reference Stitching
BIRD
Full name: Blocksworld Image Reasoning Dataset

Blocksworld Image Reasoning Dataset (BIRD) contains images of wooden blocks in different configurations, and the sequence of moves to rearrange one configuration to the other. 

Source: [Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs](https://arxiv.org/pdf/1905.12042)

Introducing paper: Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs
Almawave-SLU

Almawave-SLU is the first Italian dataset for Spoken Language Understanding (SLU). It is derived through a semi-automatic procedure and is used as a benchmark of various open source and commercial systems.

Source: [Almawave-SLU: A new dataset for SLU in Italian](https://arxiv.org/pdf/1907.07526)

Introducing paper: Almawave-SLU: A new dataset for SLU in Italian
EPIC30M

EPIC30M contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola.

Source: [EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets](https://arxiv.org/pdf/2006.08369)

Introducing paper: EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets