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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
DeepVS: A Deep Learning Based Video Saliency Prediction Approach
|
|---|---|
| Chapter number | 37 |
| Book title |
Computer Vision – ECCV 2018
|
| Published in |
arXiv, January 2018
|
| DOI | 10.1007/978-3-030-01264-9_37 |
| Book ISBNs |
978-3-03-001263-2, 978-3-03-001264-9
|
| Authors |
Lai Jiang, Mai Xu, Zulin Wang, Tie Liu, Minglang Qiao, Jiang, Lai, Xu, Mai, Liu, Tie, Qiao, Minglang, Wang, Zulin |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 141 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 27 | 19% |
| Student > Master | 22 | 16% |
| Researcher | 10 | 7% |
| Student > Doctoral Student | 8 | 6% |
| Student > Bachelor | 8 | 6% |
| Other | 13 | 9% |
| Unknown | 53 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 68 | 48% |
| Engineering | 10 | 7% |
| Agricultural and Biological Sciences | 2 | 1% |
| Economics, Econometrics and Finance | 1 | <1% |
| Arts and Humanities | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 57 | 40% |
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 06 November 2020.
All research outputs
#6,864,357
of 23,002,898 outputs
Outputs from arXiv
#145,940
of 944,042 outputs
Outputs of similar age
#137,891
of 442,250 outputs
Outputs of similar age from arXiv
#3,795
of 21,108 outputs
Altmetric has tracked 23,002,898 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 944,042 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done well, scoring higher than 84% 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 442,250 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 68% of its contemporaries.
We're also able to compare this research output to 21,108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.