From ee3d0d98e19f1d8177d85af1866fd0ee431fe9ea Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 25 Nov 2018 22:19:15 +0100 Subject: moving stuff --- .../entries/300 faces In-the-wild challenge: Database and results.csv | 1 - 1 file changed, 1 deletion(-) delete mode 100644 datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv (limited to 'datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv') diff --git a/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv b/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv deleted file mode 100644 index eaaf1a93..00000000 --- a/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv +++ /dev/null @@ -1 +0,0 @@ -300 faces in-the-wild challenge: Database and results|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0262885616000147|2016|141|9|4741451765657920988|None|http://scholar.google.com/scholar?cites=4741451765657920988&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4741451765657920988&hl=en&as_sdt=0,5|None|Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues.(a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low.(b) Most … -- cgit v1.2.3-70-g09d2