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Understanding Uncertainty Maps in Vision with Statistical Testing

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2022)10.1109/cvpr52688.2022.00050Source: DataRank Database

Understanding Uncertainty Maps in Vision with Statistical Testing is a research paper published in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022). On theSindex it has a DataRank of 0. It has been cited 5 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access5 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)OA: greenTopics (13)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Zhichun Huang ORCID,Songwong Tasneeyapant,Rudrasis Chakraborty ORCID,Vikas Singh ORCID,Jurijs Nazarovs

Live enrichment

PubMed:37222993 PMC:PMC10205027 OpenAlex:https://openalex.org/W4312723540

NIH

Grant: RF1AG059312,RF1AG062336,RF1AG059869

NSF

Grant: CCF 1918211

NIA NIH HHS

Grant: R01 AG040396

NIA NIH HHS

Grant: RF1 AG062336

NIA NIH HHS

Grant: RF1 AG059312

NIA NIH HHS

Grant: RF1 AG059869

greenLicense: https://doi.org/10.15223/policy-029

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10205027/pdf/nihms-1894544.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/9878378/9878366/09880346.pdf?arnumber=9880346

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/10205027

conferencehttps://doi.org/10.1109/cvpr52688.2022.00050

Fields of Study

Gaussian Processes and Bayesian InferenceAnomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Keywords

Computer scienceArtificial intelligenceMachine learningStatistical hypothesis testingField (mathematics)Artificial neural networkDeep neural networksStatistical modelMathematicsStatistics