Omkar2118/datasets huggingface dataset:
DPM
Full name: Don’t Patronize Me!

Don’t Patronize Me! (DPM) is an annotated dataset with Patronizing and Condescending Language towards vulnerable communities.

Introducing paper: SemEval-2022 Task 4: Patronizing and Condescending Language Detection
FPv1

FPv1 (prior name FAUST-partial) is a 3D registration benchmark dataset created to address the lack of data variability in the existing 3D registration benchmarks such as: 3DMatch, ETH, KITTI.

The original FAUST training dataset is comprised of 100 3D scans of human bodies.

The benchmark generation for a single scan from the FAUST training dataset can be summarized as follows:

1. Make xz-plane the floor by translating the minimal bounding box point of the scan to the origin 
2. Surround the scan with a regular icosahaedron. Each point of the icosahaedron acts as a viewpoint
3. For each viewpoint, create a partial point cloud using the hidden point removal algorithm

Finally, for a pair of partial point clouds with the desired overalp, generate a random rotation from the desired rotation range and translation range.

Introducing paper: Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration
jaCappella

**jaCappella** is a corpus of Japanese a cappella vocal ensembles (jaCappella corpus) for vocal ensemble separation and synthesis. It consists of 35 copyright-cleared vocal ensemble songs and their audio recordings of individual voice parts. These songs were arranged from out-of-copyright Japanese children's songs and have six voice parts (lead vocal, soprano, alto, tenor, bass, and vocal percussion). They are divided into seven subsets, each of which features typical characteristics of a music genre such as jazz and enka.

Source: [jaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus](https://arxiv.org/pdf/2211.16028v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.16028v1.pdf](https://arxiv.org/pdf/2211.16028v1.pdf)

Introducing paper: JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus
KiloGram

**KiloGram** is a resource for studying abstract visual reasoning in humans and machines. It contains a richly annotated dataset with >1k distinct stimuli.

Source: [Abstract Visual Reasoning with Tangram Shapes](https://arxiv.org/pdf/2211.16492v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.16492v1.pdf](https://arxiv.org/pdf/2211.16492v1.pdf)

Introducing paper: Abstract Visual Reasoning with Tangram Shapes
ClueWeb22

**ClueWeb22** is the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high quality, large scale web corpus to support a range of academic and industry research, for example, in information systems, retrieval-augmented AI systems, and model pretraining. Compared with earlier CLUEWeb corpora, the ClUEWeb22 corpus is larger, more varied, of higher-quality, and aligned with the document distributions in commercial web search. Besides raw HTML, the dataset includes rich information about the web pages provided by industry-standard document understanding systems, including the visual representation of pages rendered by a web browser, parsed HTML structure information from a neural network parser, and pre-processed cleaned document text.

Source: [https://arxiv.org/pdf/2211.15848v1.pdf](https://arxiv.org/pdf/2211.15848v1.pdf)

Image Source: [ClueWeb22: 10 Billion Web Documents with Rich Information](https://arxiv.org/pdf/2211.15848v1.pdf)

Introducing paper: ClueWeb22: 10 Billion Web Documents with Visual and Semantic Information
NVD
Full name: Naturalistic Variation Object Dataset

**Naturalistic Variation Object Dataset (NVD)** is a large simulated dataset of 272k images of everyday objects with naturalistic variations such as object pose, scale, viewpoint, lighting and occlusions.

Source: [Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing](https://arxiv.org/pdf/2211.16499v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.16499v1.pdf](https://arxiv.org/pdf/2211.16499v1.pdf)

Introducing paper: Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing