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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 (72nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

twitter
6 tweeters

Readers on

mendeley
7 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

Twitter Demographics

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 1 14%
Lecturer 1 14%
Student > Ph. D. Student 1 14%
Student > Master 1 14%
Student > Postgraduate 1 14%
Other 0 0%
Unknown 2 29%
Readers by discipline Count As %
Computer Science 4 57%
Business, Management and Accounting 1 14%
Unknown 2 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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
#6,399,669
of 24,417,958 outputs
Outputs from Pattern Recognition
#725
of 2,742 outputs
Outputs of similar age
#109,864
of 404,464 outputs
Outputs of similar age from Pattern Recognition
#3
of 25 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,742 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 73% 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 404,464 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 72% of its contemporaries.
We're also able to compare this research output to 25 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.