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X Demographics
Mendeley readers
Attention Score in Context
Chapter title |
From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data
|
---|---|
Chapter number | 46 |
Book title |
Computer Vision – ECCV 2018
|
Published in |
arXiv, September 2018
|
DOI | 10.1007/978-3-030-01216-8_46 |
Book ISBNs |
978-3-03-001215-1, 978-3-03-001216-8
|
Authors |
Daniel C. Castro, Sebastian Nowozin, Daniel Coelho de Castro |
X Demographics
The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 17% |
Japan | 1 | 8% |
United States | 1 | 8% |
Netherlands | 1 | 8% |
Unknown | 7 | 58% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 50% |
Scientists | 5 | 42% |
Practitioners (doctors, other healthcare professionals) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 93 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 17 | 18% |
Student > Ph. D. Student | 16 | 17% |
Researcher | 11 | 12% |
Student > Bachelor | 4 | 4% |
Other | 4 | 4% |
Other | 10 | 11% |
Unknown | 31 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 49 | 53% |
Engineering | 5 | 5% |
Mathematics | 2 | 2% |
Economics, Econometrics and Finance | 2 | 2% |
Neuroscience | 1 | 1% |
Other | 3 | 3% |
Unknown | 31 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2019.
All research outputs
#3,790,157
of 24,002,307 outputs
Outputs from arXiv
#68,413
of 1,011,770 outputs
Outputs of similar age
#71,296
of 339,604 outputs
Outputs of similar age from arXiv
#1,748
of 24,323 outputs
Altmetric has tracked 24,002,307 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,011,770 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 339,604 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 24,323 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.