Omkar2118/datasets huggingface dataset:
ObjectFolder Real

The **ObjectFolder Real** dataset contains multisensory data collected from 100 real-world household objects. The visual data for each object include three high-quality 3D meshes of different resolutions and an HD video recording of the object rotating in a lightbox; The acoustic data for each object include impact sound recordings recorded at 30–50 points of the object, each of which is 6s long and is accompanied by the coordinate of the striking location on the object mesh, ground-truth contact force profile, and the accompanying video for the impact. The tactile data for each object include tactile readings at the same 30–50 points of the object, with each tactile reading as a video of the tactile RGB images that record the entire gel deformation process and is accompanied by two videos of the contact process from an in-hand camera and a third-view camera.

Source: [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://arxiv.org/pdf/2306.00956v1.pdf)

Image Source: [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://arxiv.org/pdf/2306.00956v1.pdf)

Introducing paper: The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects
gRefCOCO

gRefCOCO is the first large-scale Generalized Referring Expression Segmentation dataset that contains multi-target, no-target, and single-target expressions.
AMI Meeting Corpus

The **AMI Meeting Corpus** is a **multi-modal data set** comprising **100 hours of meeting recordings**. It has been meticulously curated for research purposes and includes various modes of data capture. Let me provide you with more details:

1. **Purpose and Context**:
   - The corpus was created in the context of a project that aims to develop **meeting browsing technology**.
   - Eventually, it will be **publicly released** for use by researchers and practitioners.

2. **Data Composition**:
   - **100 hours** of recorded meetings are included.
   - The data is collected from **various domains** and scenarios.
   - Around **two-thirds** of the data involves participants playing different roles in a **design team**, taking a design project from kick-off to completion over the course of a day.
   - The remaining data consists of **naturally occurring meetings**.

3. **Modalities and Annotations**:
   - The corpus includes **synchronized recording devices** such as close-talking and far-field microphones, individual and room-view video cameras, projection, a whiteboard, and individual pens.
   - Annotations cover various phenomena, including **orthographic transcription**, **dialog acts**, and **head movement**.

4. **Research Applications**:
   - Although initially designed for meeting browsing technology, the AMI Meeting Corpus is **useful for a wide range of research areas**.
   - Researchers engaged in **video processing** can access higher resolution videos.

Source: Conversation with Bing, 3/16/2024
(1) AMI Corpus - University of Edinburgh. https://groups.inf.ed.ac.uk/ami/corpus/.
(2) The AMI Meeting Corpus: A Pre-announcement. https://www.research.ed.ac.uk/en/publications/the-ami-meeting-corpus-a-pre-announcement.
(3) The AMI Meeting Corpus: A Pre-announcement | SpringerLink. https://link.springer.com/chapter/10.1007/11677482_3.
(4) The AMI Meeting Corpus: A Pre-announcement — University of Twente .... https://research.utwente.nl/en/publications/the-ami-meeting-corpus-a-pre-announcement.
ICSI Meeting Corpus

ICSI Meeting Corpus in JSON format.

Introducing paper: Abstractive Meeting Summarization: A Survey
ELITR Minuting Corpus

ELITR Minuting Corpus in JSON format.

Introducing paper: Abstractive Meeting Summarization: A Survey
BabySLM

**BabySLM** is a language-acquisition-friendly benchmark to probe speech-based LMs at the lexical and syntactic levels, both of which are compatible with the vocabulary typical of children's language experiences.

Source: [BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models](https://arxiv.org/pdf/2306.01506v1.pdf)

Image Source: [BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models](https://arxiv.org/pdf/2306.01506v1.pdf)

Introducing paper: BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
CovidET-EXT

CovidET-EXT is a dataset that augments Zhan et al. (2022)'s abstractive dataset CovidET (in the context of the COVID-19 crisis) with extractive triggers. The result is a dataset of 1,883 Reddit posts about the COVID-19 pandemic, manually annotated with 7 fine-grained emotions (from CovidET) and their corresponding extractive triggers.

Introducing paper: Unsupervised Extractive Summarization of Emotion Triggers
Unidecor
Full name: A unified deception corpus for cross-corpus deception detection.

UNIDECOR is a unified corpus consolidating publicly available textual deception datasets into a common format.

Introducing paper: UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
ARO
Full name: ARO

Attribution, Relation, and Order (ARO) benchmark to systematically evaluate the ability of VLMs to understand different types of relationships, attributes, and order information. ARO consists of Visual Genome Attribution, to test the understanding of objects' properties; Visual Genome Relation, to test for relational understanding; and COCO-Order & Flickr30k-Order, to test for order sensitivity in VLMs. ARO is orders of magnitude larger than previous benchmarks of compositionality, with more than 50,000 test cases.
PanCollection

Pansharpening Datasets from WorldView 2, WorldView 3, QuickBird, Gaofen 2 sensors.
FinRED

**FinRED** is a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method.

Introducing paper: FinRED: A Dataset for Relation Extraction in Financial Domain
A Game Of Sorts

**A Game Of Sorts** is a collaborative image ranking task. Players are asked to rank a set of images based on a given sorting criterion. The game provides a framework for the evaluation of visually grounded language understanding and generation of referring expressions in multimodal dialogue settings.

Introducing paper: Collecting Visually-Grounded Dialogue with A Game Of Sorts