Constrained Deep Transfer Feature Learning and Its Applications is a research paper published in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). On theSindex it has a DataRank of 0.550. It has been cited 38 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
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Base Score Contribution
0.550
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Science Foundation
Grant: 1205664
CI-ADDO-EN: Collaborative Research: 3D Dynamic Multimodal Spontaneous Emotion Corpus for Automated Facial Behavior and Emotion Analysis
FWCI
3.49
Citation Percentile
0.9%
Citation Trend
Fields of Study
Keywords