Omkar2118/datasets huggingface dataset:
ALPIX-VSR
Full name: Dataset

we collected a new real-world dataset, called ALPIXVSR, using a ALPIX-Eiger event camera1
. The camera outputs well aligned RGB frames and events. The RGB frames
enjoy a resolution of 3264 × 2448 and are generated by a
carefully designed image signal processor(ISP) from RAW
data with the Quad Bayer pattern , and the events have
a resolution with 1632 × 1224.

https: //vlis2022.github.io/cvpr23/egvsr

Introducing paper: Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution
BEAR
Full name: Benchmark on video Action Recognition

**BEAR (Benchmark on video Action Recognition)** is a collection of 18 video datasets grouped into 5 categories (anomaly, gesture, daily, sports, and instructional), which covers a diverse set of real-world applications.

Source: [A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition](https://arxiv.org/pdf/2303.13505v1.pdf)

Image Source: [A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition](https://arxiv.org/pdf/2303.13505v1.pdf)

Introducing paper: A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition
VR-Folding

**VR-Folding** contains garment meshes of 4 categories from CLOTH3D dataset, namely Shirt, Pants, Top and Skirt. For flattening task, there are 5871 videos which contain 585K frames in total. For folding task, there are 3896 videos which contain 204K frames in total. The data for each frame include multi-view RGB-D images, object masks, full garment meshes, and hand poses.

Source: [GarmentTracking: Category-Level Garment Pose Tracking](https://arxiv.org/pdf/2303.13913v1.pdf)

Image Source: [GarmentTracking: Category-Level Garment Pose Tracking](https://arxiv.org/pdf/2303.13913v1.pdf)

Introducing paper: GarmentTracking: Category-Level Garment Pose Tracking
MMHS150k
Full name: Multimodal Hate Speech

Existing hate speech datasets contain only textual data. We create a new manually annotated multimodal hate speech dataset formed by 150,000 tweets, each one of them containing text and an image. We call the dataset MMHS150K.

Introducing paper: Exploring Hate Speech Detection in Multimodal Publications