Omkar2118/datasets huggingface dataset:
Conversational Stance Detection

**Conversational Stance Detection (CSD)** is a dataset with annotations of stances and the structures of conversation threads. It consists of 500 conversation threads (including 500 posts and 5376 comments) from six major social media platforms in Hong Kong.

Source: [Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads](https://arxiv.org/pdf/2211.03061v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.03061v1.pdf](https://arxiv.org/pdf/2211.03061v1.pdf)

Introducing paper: Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads
SciHTC

**SciHTC** is a dataset for hierarchical multi-label text classification (HMLTC) of scientific papers which contains 186,160 papers and 1,233 categories from the ACM CCS tree.

Source: [Hierarchical Multi-Label Classification of Scientific Documents](https://arxiv.org/pdf/2211.02810v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.02810v1.pdf](https://arxiv.org/pdf/2211.02810v1.pdf)

Introducing paper: Hierarchical Multi-Label Classification of Scientific Documents
CochlScene

**CochlScene** is a dataset for acoustic scene classification. The dataset consists of 76k samples collected from 831 participants in 13 acoustic scenes.

Source: [CochlScene: Acquisition of acoustic scene data using crowdsourcing](https://arxiv.org/pdf/2211.02289v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.02289v1.pdf](https://arxiv.org/pdf/2211.02289v1.pdf)

Introducing paper: CochlScene: Acquisition of acoustic scene data using crowdsourcing
NJH
Full name: Not Just Hate

**NJH** is a dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance. It is a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content.

Source: ["It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online](https://arxiv.org/pdf/2210.15870v1.pdf)

Image Source: [https://arxiv.org/pdf/2210.15870v1.pdf](https://arxiv.org/pdf/2210.15870v1.pdf)

Introducing paper: "It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online
MCXFACE
Full name: Multi-Channel Heterogeneous Face Recognition dataset

MCXFace is a heterogeneous face recognition dataset consisting of multi-channel image samples for 51 subjects. For each subject color (RGB), thermal, near-infrared (850 nm), short-wave infrared (1300 nm), Depth, Stereo depth, and depth estimated from RGB images are available. Overall 7406 images together with landmark annotations and standard protocols are available in this dataset.

The Multi-Channel Heterogeneous Face Recognition dataset (MCXFace) is derived from the HQ-WMCA dataset (https://www.idiap.ch/en/dataset/hq-wmca). The MCXFace dataset contains images of 51 subjects collected in different channels under three different sessions and various illumination conditions. The channels available are color (RGB), thermal, near-infrared (850 nm), short-wave infrared (1300 nm), Depth, Stereo depth, and depth estimated from RGB images using the 3DDFA method. All the channels are registered spatially and temporally across all the modalities. In the MCXFace dataset, only bonafide samples are present. Further, the files are divided into train and dev sets with a disjoint set of identities to make experiments in different homogeneous and heterogeneous settings possible. For each of the protocols, we have created five different folds, by randomly dividing the subjects into train and dev partitions. In addition to the images, annotations for left and right eye centers for all the images are also provided in a JSON file. Specifically for each image, annotations for the left eye, right eye, mouth left corner, mouth right corner, nose, top left, and top right are provided. Each image file is stored as a ``.jpg'' file with a resolution of 1920x1200.

Introducing paper: Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
MEVID
Full name: Multi-view Extended Videos with Identities Dataset

**Multi-view Extended Videos with Identities dataset (MEVID)** is a dataset for large-scale, video person re-identification (ReID) in the wild. It spans an extensive indoor and outdoor environment across nine unique dates in a 73-day window, various camera viewpoints, and entity clothing changes. Specifically, it contains labels of the identities of 158 unique people wearing 598 outfits taken from 8, 092 tracklets, average length of about 590 frames, seen in 33 camera views from the very large-scale MEVA person activities dataset.

Source: [MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification](https://arxiv.org/pdf/2211.04656v1.pdf)

Image Source: [https://arxiv.org/pdf/2211.04656v1.pdf](https://arxiv.org/pdf/2211.04656v1.pdf)

Introducing paper: MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification