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diff --git a/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv b/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv new file mode 100644 index 00000000..249cea3a --- /dev/null +++ b/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv @@ -0,0 +1 @@ +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evaluation datasets for face detection, which is of great value in real-world applications, are still somewhat out-of-date.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 … |