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authoradamhrv <adam@ahprojects.com>2018-12-15 19:57:49 +0100
committeradamhrv <adam@ahprojects.com>2018-12-15 19:57:49 +0100
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treea8784b7ec2bc5a0451c252f66a6b786f3a2504f5 /scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv
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parentc5b02ffab8d388e8a2925e51736b902a48a95e71 (diff)
Merge branch 'master' of github.com:adamhrv/megapixels_dev
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+Fine-grained evaluation on face detection in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7163158/|2015|24|7|6318135921321197431|None|http://scholar.google.com/scholar?cites=6318135921321197431&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6318135921321197431&hl=en&as_sdt=0,5|None|Current evaluation datasets for face detection, which is of great value in real-world applications, are still somewhat out-of-date. We propose a new face detection dataset MALF (short for Multi-Attribute Labelled Faces), which contains 5,250 images collected from the Internet and~ 12,000 labelled faces. The MALF dataset highlights in two main features: 1) It is the largest dataset for evaluation of face detection in the wild, and the annotation of multiple facial attributes makes it possible for fine-grained performance analysis. 2) To …