Omkar2118/datasets huggingface dataset:
DoMSEV
Full name: Dataset of Multimodal Semantic Egocentric Video

The Dataset of Multimodal Semantic Egocentric Video (DoMSEV) contains 80-hours of multimodal (RGB-D, IMU, and GPS) data related to First-Person Videos with annotations for recorder profile, frame scene, activities, interaction, and attention.

Source: [A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos](/paper/a-weighted-sparse-sampling-and-smoothing-1)

Introducing paper: A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos
DoQA

A dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing.

Source: [DoQA -- Accessing Domain-Specific FAQs via Conversational QA](/paper/doqa-accessing-domain-specific-faqs-via)

Introducing paper: DoQA -- Accessing Domain-Specific FAQs via Conversational QA
DOTmark
Full name: Discrete Optimal Transport Benchmark

DOTmark is a benchmark for discrete optimal transport, which is designed to serve as a neutral collection of problems, where discrete optimal transport methods can be tested, compared to one another, and brought to their limits on large-scale instances. It consists of a variety of grayscale images, in various resolutions and classes, such as several types of randomly generated images, classical test images and real data from microscopy.

Source: [DOTmark - A Benchmark for Discrete Optimal Transport](/paper/dotmark-a-benchmark-for-discrete-optimal)

Introducing paper: DOTmark - A Benchmark for Discrete Optimal Transport
DPC-Captions

This is an open-source image captions dataset for the aesthetic evaluation of images.
The dataset is called **DPC-Captions**, which contains comments of up to five aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset.

Source: [https://github.com/BestiVictory/DPC-Captions](https://github.com/BestiVictory/DPC-Captions)

Introducing paper: Aesthetic Attributes Assessment of Images
DpgMedia2019

**DpgMedia2019** is a Dutch news dataset for partisanship detection. It contains more than 100K articles that are labelled on the publisher level and 776 articles that were crowdsourced using an internal survey platform and labelled on the article level.

Source: [https://github.com/dpgmedia/partisan-news2019](https://github.com/dpgmedia/partisan-news2019)

Introducing paper: DpgMedia2019: A Dutch News Dataset for Partisanship Detection
DramaQA

The DramaQA focuses on two perspectives: 1) Hierarchical QAs as an evaluation metric based on the cognitive developmental stages of human intelligence. 2) Character-centered video annotations to model local coherence of the story. The dataset is built upon the TV drama "Another Miss Oh" and it contains 17,983 QA pairs from 23,928 various length video clips, with each QA pair belonging to one of four difficulty levels.

Source: [DramaQA: Character-Centered Video Story Understanding with Hierarchical QA](/paper/dramaqa-character-centered-video-story)

Introducing paper: DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
Dreaddit

Consists of 190K posts from five different categories of Reddit communities.

Source: [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](/paper/dreaddit-a-reddit-dataset-for-stress-analysis-1)

Introducing paper: Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
Drive&Act

The Drive&Act dataset is a state of the art multi modal benchmark for driver behavior recognition. The dataset includes 3D skeletons in addition to frame-wise hierarchical labels of 9.6 Million frames captured by 6 different views and 3 modalities (RGB, IR and depth).

It offers following key features:

* 12h of video data in 29 long sequences
* Calibrated multi view camera system with 5 views
* Multi modal videos: NIR, Depth and Color data
* Markerless motion capture: 3D Body Pose and Head Pose
* Model of the static interior of the car
* 83 manually annotated hierarchical activity labels:
    * Level 1: Long running tasks (12)
    * Level 2: Semantic actions (34)
    * Level 3: Object Interaction tripplets [action|object|location] (6|17|14)

Source: [Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles](/paper/driveact-a-multi-modal-dataset-for-fine)

Introducing paper: Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles
DrivingStereo

DrivingStereo contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points. 

Source: [DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios](/paper/drivingstereo-a-large-scale-dataset-for)
Image Source: [https://drivingstereo-dataset.github.io/](https://drivingstereo-dataset.github.io/)

Introducing paper: DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios
Drone Tracking

This dataset contains videos where a flying drone (hexacopter) is captured with multiple consumer-grade cameras (smartphones, compact cameras, gopro,...) with highly accurate 3D drone trajectory ground truth recorderd by a precise real-time RTK system from Fixposition. In some videos, the ground truth temporal synchronization and ground truth camera locations are also provided.

Source: [https://github.com/CenekAlbl/drone-tracking-datasets](https://github.com/CenekAlbl/drone-tracking-datasets)
Image Source: [https://github.com/CenekAlbl/drone-tracking-datasets](https://github.com/CenekAlbl/drone-tracking-datasets)

Introducing paper: Reconstruction of 3D flight trajectories from ad-hoc camera networks
DSBI
Full name: Double-Sided Braille Image

The **Double-Sided Braille Image** dataset (**DSBI**) is a large-scale dataset for Braille image recognition. It has detailed Braille recto dots, verso dots and Braille cells annotation.

Source: [https://arxiv.org/abs/1811.10893](https://arxiv.org/abs/1811.10893)
Image Source: [https://github.com/yeluo1994/DSBI](https://github.com/yeluo1994/DSBI)

Introducing paper: DSBI: Double-Sided Braille Image Dataset and Algorithm Evaluation for Braille Dots Detection
dSprites
Full name: Disentanglement testing Sprites dataset

dSprites is a dataset of 2D shapes procedurally generated from 6 ground truth independent latent factors. These factors are color, shape, scale, rotation, x and y positions of a sprite.

All possible combinations of these latents are present exactly once, generating N = 737280 total images.

Source: [dSprites](https://github.com/deepmind/dsprites-dataset)

Introducing paper: beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Dunhuang Grottoes Painting Dataset

This dataset provides a large number of training and testing example which is sufficient for a deep learning approach to address Dunhuang Grotto Painting restoration.

Source: [Dunhuang Grottoes Painting Dataset and Benchmark](/paper/dunhuang-grotto-painting-dataset-and)

Introducing paper: Dunhuang Grottoes Painting Dataset and Benchmark
DuRecDial

A human-to-human Chinese dialog dataset (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). 

Source: [Towards Conversational Recommendation over Multi-Type Dialogs](/paper/towards-conversational-recommendation-over)

Introducing paper: Towards Conversational Recommendation over Multi-Type Dialogs
DVS128 Gesture

Comprises 11 hand gesture categories from 29 subjects under 3 illumination conditions.

Source: [A Low Power, Fully Event-Based Gesture Recognition System](/paper/a-low-power-fully-event-based-gesture)

Introducing paper: A Low Power, Fully Event-Based Gesture Recognition System