Omkar2118/datasets huggingface dataset:
NarraSum

**NarraSum** is a large-scale narrative summarization dataset. It contains 122K narrative documents, which are collected from plot descriptions of movies and TV episodes with diverse genres, and their corresponding abstractive summaries.

Source: [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://arxiv.org/pdf/2212.01476v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.01476v1.pdf](https://arxiv.org/pdf/2212.01476v1.pdf)

Introducing paper: NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
Morphosyntactic-analysis-dataset

This dataset is for evaluation of morphosyntactic analyzers.
Wireless AI Research Dataset

Wireless AI Research Dataset is a flexible and easy-to-use dataset with realistic environments designed for various wireless AI tasks. It supports sensing tasks such as localization and environment reconstruction, MIMO tasks such as reflection system and beam-forming, and PHY tasks such as CSI feedback and channel estimation
Imbalanced-MiniKinetics200
Full name: Imbalanced-MiniKinetics200

**Imbalanced-MiniKinetics200** was proposed by "Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition" to evaluate varying scenarios of video long-tailed recognition. Similar to CIFAR-10/100-LT, it utilizes an imbalance factor to construct long-tailed variants of the MiniKinetics200 dataset. **Imbalanced-MiniKinetics200** is a subset of Mini-Kinetics-200 consisting of 200 categories which is also a subset of Kinetics400.
Both the raw frames and extracted features with ResNet50/101 are provided.

Introducing paper: Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition
DialogCC

**DialogCC** is a large-scale multi-modal dialogue dataset, which covers diverse real-world topics and various images per dialogue. It contains 651k unique images and is designed for image and text retrieval tasks.

Source: [DialogCC: Large-Scale Multi-Modal Dialogue Dataset](https://arxiv.org/pdf/2212.04119v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.04119v1.pdf](https://arxiv.org/pdf/2212.04119v1.pdf)

Introducing paper: DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset
3D FRONT HUMAN

**3D FRONT HUMAN** is a dataset that extends the large-scale synthetic scene dataset 3D-FRONT. Specifically, the 3D scenes with humans, i.e., non-contact humans (a sequence of walking motion and standing humans) as well as contact humans (sitting, touching, and lying humans). 3D FRONT HUMAN contains four room types: 1) 5689 bedrooms, 2) 2987 living rooms, 3) 2549 dining rooms and 4) 679 libraries. We use 21 object categories for the bedrooms, 24 for the living and dining rooms, and 25 for the libraries.

Source: [MIME: Human-Aware 3D Scene Generation](https://arxiv.org/pdf/2212.04360v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.04360v1.pdf](https://arxiv.org/pdf/2212.04360v1.pdf)

Introducing paper: MIME: Human-Aware 3D Scene Generation
Tragic Talkers

**Tragic Talkers** is an audio-visual dataset consisting of excerpts from the "Romeo and Juliet" drama captured with microphone arrays and multiple co-located cameras for light-field video. Tragic Talkers provides ideal content for object-based media (OBM) production. It is designed to cover various conventional talking scenarios, such as monologues, two-people conversations, and interactions with considerable movement and occlusion, yielding 30 sequences captured from a total of 22 different points of view and two 16-element microphone arrays.

Source: [Tragic Talkers: A Shakespearean Sound- and Light-Field Dataset for Audio-Visual Machine Learning Research](https://arxiv.org/pdf/2212.01892v1.pdf)

Image Source: [https://arxiv.org/pdf/2212.01892v1.pdf](https://arxiv.org/pdf/2212.01892v1.pdf)

Introducing paper: Tragic Talkers: A Shakespearean Sound- and Light-Field Dataset for Audio-Visual Machine Learning Research
Anatomy of Video Editing (AVE)

Machine learning is transforming the video editing industry. Recent advances in computer vision have leveled-up video editing tasks such as intelligent reframing, rotoscoping, color grading, or applying digital makeups. However, most of the solutions have focused on video manipulation and VFX. This work introduces the Anatomy of Video Editing, a dataset, and benchmark, to foster research in AI-assisted video editing. Our benchmark suite focuses on video editing tasks, beyond visual effects, such as automatic footage organization and assisted video assembling. To enable research on these fronts, we annotate more than 1.5M tags, with relevant concepts to cinematography, from 196176 shots sampled from movie scenes. We establish competitive baseline methods and detailed analyses for each of the tasks. We hope our work sparks innovative research towards underexplored areas of AI-assisted video editing.

Introducing paper: The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing
HM3DSem

The Habitat-Matterport 3D Semantics Dataset (HM3DSem) is the largest-ever dataset of 3D real-world and indoor spaces with densely annotated semantics that is available to the academic community. HM3DSem v0.2 consists of 142,646 object instance annotations across 216 3D-spaces from HM3D and 3,100 rooms within those spaces. The HM3D scenes are annotated with the 142,646 raw object names, which are mapped to 40 Matterport categories. On average, each scene in HM3DSem v0.2 consists of 661 objects from 106 categories. This dataset is the result of 14,200+ hours of human effort for annotation and verification by 20+ annotators.

HM3DSem v0.2 is free and available here for academic, non-commercial research. Researchers can use it with FAIR’s Habitat simulator to train embodied agents, such as home robots and AI assistants, at scale for semantic navigation tasks. HM3DSem v0.1 was also the basis of the recently concluded Habitat 2022 ObjectNav challenge. Please see our arxiv report for more details.

Introducing paper: Habitat-Matterport 3D Semantics Dataset