Omkar2118/datasets huggingface dataset:
CelebV-Text

**CelebV-Text** comprises 70,000 in-the-wild face video clips with diverse visual content, each paired with 20 texts generated using the proposed semi-automatic text generation strategy. The provided texts describes both static and dynamic attributes precisely.

Source: [CelebV-Text: A Large-Scale Facial Text-Video Dataset](https://arxiv.org/pdf/2303.14717v1.pdf)

Image Source: [CelebV-Text: A Large-Scale Facial Text-Video Dataset](https://arxiv.org/pdf/2303.14717v1.pdf)

Introducing paper: CelebV-Text: A Large-Scale Facial Text-Video Dataset
IHDP
Full name: Infant Health and Development Program

The Infant Health and Development Program (IHDP) is a randomized controlled study designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The datasets is first used for benchmarking treatment effect estimation algorithms in Hill [35], where selection bias is induced by removing non-random subsets of the treated individuals to create an observational dataset, and the outcomes are generated using the original covariates and treatments. It contains 747 subjects and 25 variables.

Introducing paper: Estimating individual treatment effect: generalization bounds and algorithms
Jobs

The Jobs dataset by LaLonde [36] is a widely used benchmark in the causal inference community, where the treatment is job training and the outcomes are income and employment status after training. The dataset includes 8 covariates such as age, education, and previous earnings. Our goal is to predict unemployment, using the feature set of Dehejia and Wahba [37]. Following Shalit et al. [8], we combined the LaLonde experimental sample (297 treated, 425 control) with the PSID comparison group (2490 control).

Introducing paper: Estimating individual treatment effect: generalization bounds and algorithms
Hi4D

**Hi4D** contains 4D textured scans of 20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D contains rich interaction centric annotations in 2D and 3D alongside accurately registered parametric body models.

Source: [Hi4D: 4D Instance Segmentation of Close Human Interaction](https://arxiv.org/pdf/2303.15380v1.pdf)

Image Source: [Hi4D: 4D Instance Segmentation of Close Human Interaction](https://arxiv.org/pdf/2303.15380v1.pdf)

Introducing paper: Hi4D: 4D Instance Segmentation of Close Human Interaction