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Dataset results
6 results for “Human-Centric”
AUTH-OpenDR Mixed Image Annotated Dataset for Human-centric Perception Tasks
<p>The dataset was generated through a mixed (real and synthetic) image data generation method which utilizes real background images and DL-generated human models. It contains 50000 real images depicting urban scenes, populated by synthetic human models in various positions and poses and is suitable for training/evaluating (a) pose estimation, (b) person detection, (c) identity recognition methods. Annotations for 2D bounding boxes of the depicted humans, their IDs and 2D keypoints etc are provided. The 133 3D human models, required by the method, were generated using the Pixel-aligned Implicit Function (PIFu) and full-body images of people from the Clothing Co-Parsing (CCP) dataset. As background images, a subset of the Cityscapes dataset was used. The Cityscapes license prohibits the distribution of any modified versions of itself. Thus, we provide code that can re-generate the exact same dataset, given that the Cityscapes dataset is downloaded by the website of its authors.</p> <p>Code and instructions for re-generating the dataset are provided <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation/human_dataset_generation">here</a>.</p> <p>The dataset was developed by Aristotle University of Thessaloniki (AUTH) within the H2020 OpenDR Project.</p>
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
<p>This dataset is provided for the paper "Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data".</p> <p>Please refer to the readme in the zipped file for additional documentation.</p> <p>The zipped file contains two CSV files for every country in Africa obtained by queries "[country name]" and "[country name + people]".</p>
A View From Somewhere: Human-Centric Face Representations
<p><strong>Abstract</strong></p> <p>Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between same-labeled faces. To address these issues, we present A View From Somewhere (AVFS)---a dataset of 638,180 human judgments of face similarity. We demonstrate the utility of AVFS for learning a continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under a novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human-interpretable decomposition of the dimensions used in the human decision-making process, and the importance distinct annotators place on each dimension. We additionally show the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.</p> <p><strong>ICLR 2023 Paper</strong></p> <p><a href="https://arxiv.org/abs/2303.17176">arxiv.org/abs/2303.17176</a></p> <p><strong>Code</strong></p> <p><a href="https://github.com/SonyResearch/a_view_from_somewhere">github.com/SonyResearch/a_view_from_somewhere</a></p> <p><strong>Citing A View From Somewhere</strong></p> <pre>@inproceedings{ andrews2023avfs, title={A View From Somewhere: Human-Centric Face Representations}, author={Jerone T A Andrews and Przemyslaw Joniak and Alice Xiang}, booktitle={ICLR}, year={2023} }</pre>
AH-CID: A Tool to Automatically Detect Human-Centric Issues in App Reviews
<p>The keyword list used in the paper to pre-filter the app reviews.</p>
Supporting Developers in Addressing Human-centric Issues in Mobile Apps
<p>A replication package including a manually analysed dataset of a random sample of 1,200 app reviews and 1,200 issue comments from 12 diverse projects that exist on both Google App Store and GitHub, the results of our machine learning and deep learning approaches, our survey questions and raw responses from app developers to the survey.</p>
To What Extent Do Developers Discuss End User Human-Centric Issues of Software on GitHub?
<p>Labelled dataset of a random selection of 1230 issue comments from 7 GitHub repositories.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.