Omkar2118/datasets huggingface dataset:
Winoground

Winoground is a dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning. Given two images and two captions, the goal is to match them correctly -- but crucially, both captions contain a completely identical set of words, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich set of fine-grained tags to assist in analyzing model performance.

Introducing paper: Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality
MCoNaLa
Full name: Multilingual CoNaLa

**MCoNaLa** is a multilingual dataset to benchmark code generation from natural language commands extending beyond English. Modeled off of the methodology from the English Code/Natural Language Challenge (CoNALa) dataset, the authors annotated a total of 896 NL-code pairs in three languages: Spanish, Japanese, and Russian. 

Due to the limited sample of multiple languages, we use English CoNaLa samples for training, where the intents are originally written in English. Spanish, Japanese, and Russian are of the Target Language (TL), whose samples are always (only) used for testing purpose due to the limited amount.

English is the High-Resource Language (HRL) for which the samples can be leveraged for model training.
Kinetics-GEB+

**Kinetics-GEB+** (Generic Event Boundary Captioning, Grounding and Retrieval) is a dataset that consists of over 170k boundaries associated with captions describing status changes in the generic events in 12K videos.

Introducing paper: GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval
DeToxy
Full name: DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances

**DeToxy** is a publicly available toxicity annotated dataset for the English language. DeToxy is sourced from various openly available speech databases and consists of over 2 million utterances. The dataset would act as a benchmark for the relatively new and un-explored Spoken Language Processing task of detecting toxicity from spoken utterances and boost further research in this space.

Introducing paper: DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances
Animal Kingdom

Animal Kingdom is a large and diverse dataset that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors. The wild animal footage used in the dataset records different times of the day in an extensive range of environments containing variations in backgrounds, viewpoints, illumination and weather conditions. More specifically, the dataset contains 50 hours of annotated videos to localize relevant animal behavior segments in long videos for the video grounding task, 30K video sequences for the fine-grained multi-label action recognition task, and 33K frames for the pose estimation task, which correspond to a diverse range of animals with 850 species across 6 major animal classes.

Introducing paper: Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding
BEHAVE

BEHAVE is a full body human-object interaction dataset with multi-view RGBD frames and corresponding 3D SMPL and object fits along with the annotated contacts between them. Dataset contains ~15k frames at 5 locations with 8 subjects performing a wide range of interactions with 20 common objects.

Introducing paper: BEHAVE: Dataset and Method for Tracking Human Object Interactions
Bamboo

Bamboo Dataset is a mega-scale and information-dense dataset for both classification and detection pre-training. It is built upon integrating **24** public datasets (e.g. **ImagenNet**, **Places365**, **Object365**, **OpenImages**) and added new annotations through **active learning**.  Bamboo has 69M image classification annotations and 32M object bounding boxes.

Introducing paper: Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
Visual Affordance Learning

A large-scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories and 35 cluttered/complex scenes with different objects and multiple affordances. To the best of our knowledge, this is the first ever and the largest multi-view RGBD visual affordance learning dataset.

Introducing paper: A large scale multi-view RGBD visual affordance learning dataset