Omkar2118/datasets huggingface dataset:
VOST

**VOST** consists of more than 700 high-resolution videos, captured in diverse environments, which are 20 seconds long on average and densely labeled with instance masks. A careful, multi-step approach is adopted to ensure that these videos focus on complex transformations, capturing their full temporal extent.

Source: [Breaking the “Object” in Video Object Segmentation](https://arxiv.org/pdf/2212.06200v1.pdf)

Image Source: [https://www.vostdataset.org/](https://www.vostdataset.org/)

Introducing paper: Breaking the "Object" in Video Object Segmentation
MAPS-KB

**MAPS-KB** is a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. It is designed for the tasks of simile detection and component extraction.

Source: [](https://arxiv.org/pdf/2212.05254v1.pdf)

Introducing paper: MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base
FLAG3D

**FLAG3D** is a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments.

Source: [FLAG3D: A 3D Fitness Activity Dataset with Language Instruction](https://arxiv.org/pdf/2212.04638v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.04638v1.pdf](https://arxiv.org/pdf/2212.04638v1.pdf)

Introducing paper: FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
Selection from FFHQ & StyleGAN2:FFHQ (used in "Testing Human Ability To Detect Deepfake Images of Human Faces" study)

This dataset is the image stimulus pool of 50 deepfake and 50 real images, used for the experiment in the study titled "Testing Human Ability To Detect Deepfake Images of Human Faces". 

Images were obtained through random selection from the Flickr Faces High Quality dataset (https://paperswithcode.com/dataset/ffhq) and likewise from output of the StyleGAN2 algorithm (https://paperswithcode.com/method/stylegan2) as trained on the FFHQ dataset. 

Also included are the 20 deepfake images, similarly obtained (from StyleGAN2:FFHQ), which were used in the familiarization intervention in the study; and the 20 deepfake images (different images but similarly obtained, albeit with an element of curation to select for images with the specific "tell-tale signs" / "visible rendering artefacts") which were used in the advice intervention in the study.

The 50 real and 50 deepfake images that were used as test stimuli in the experiment are in /real and /fake respectively; the 20 familiarization images are in /familiarization; and the 20 images used in the advice intervention are in /advice.

Introducing paper: Testing Human Ability To Detect Deepfake Images of Human Faces
MOPRD

**MOPRD**, a multidisciplinary open peer review dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions from 6578 papers.

Source: [MOPRD: A multidisciplinary open peer review dataset](https://arxiv.org/pdf/2212.04972v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.04972v1.pdf](https://arxiv.org/pdf/2212.04972v1.pdf)

Introducing paper: MOPRD: A multidisciplinary open peer review dataset