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ICC++: Explainable feature learning for art history using image compositions

Overview of attention for article published in Pattern Recognition, April 2023
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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5 X users

Citations

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2 Dimensions

Readers on

mendeley
12 Mendeley
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Title
ICC++: Explainable feature learning for art history using image compositions
Published in
Pattern Recognition, April 2023
DOI 10.1016/j.patcog.2022.109153
Authors

Prathmesh Madhu, Tilman Marquart, Ronak Kosti, Dirk Suckow, Peter Bell, Andreas Maier, Vincent Christlein

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 25%
Student > Ph. D. Student 2 17%
Lecturer > Senior Lecturer 1 8%
Lecturer 1 8%
Researcher 1 8%
Other 1 8%
Unknown 3 25%
Readers by discipline Count As %
Computer Science 5 42%
Social Sciences 2 17%
Business, Management and Accounting 1 8%
Arts and Humanities 1 8%
Unknown 3 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 November 2022.
All research outputs
#8,114,725
of 26,445,299 outputs
Outputs from Pattern Recognition
#817
of 2,976 outputs
Outputs of similar age
#138,491
of 431,791 outputs
Outputs of similar age from Pattern Recognition
#7
of 33 outputs
Altmetric has tracked 26,445,299 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 2,976 research outputs from this source. They receive a mean Attention Score of 3.9. This one has gotten more attention than average, scoring higher than 72% 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 431,791 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.