Hierarchical Binary CNNs for Landmark Localization with Limited Resources
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 535
- Type
- D - Journal article
- DOI
-
10.1109/TPAMI.2018.2866051
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 343
- Volume
- 42
- Issue
- 2
- ISSN
- 0162-8828
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- An invited paper to a special IEEE TPAMI issue which contains the best papers of the IEEE International Conference on Computer Vision (ICCV) 2017. The paper is the journal version of the ICCV 2017 paper “Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources,” which was selected to be part of the pool where the awards for ICCV 2017 were chosen from. There were 12 contenders, so overall the paper was among the best 12 papers out of 2143 submissions (top 0.5%). Both journal and conference papers originated from EPSRC project EP/M02153X/1 (2015-16, £98.6k).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -