From 93b3392d9346226c328ea2a878ff968d0303f826 Mon Sep 17 00:00:00 2001 From: "jules@lens" Date: Wed, 31 Oct 2018 02:14:14 +0100 Subject: data --- .../DEX: Deep EXpectation of apparent age from a single image.csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv (limited to 'datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv') diff --git a/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv new file mode 100644 index 00000000..b3728548 --- /dev/null +++ b/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv @@ -0,0 +1 @@ +Dex: Deep expectation of apparent age from a single image|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/html/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.html|2015|155|15|12384435539194835187|None|http://scholar.google.com/scholar?cites=12384435539194835187&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12384435539194835187&hl=en&as_sdt=0,5|None|In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the … -- cgit v1.2.3-70-g09d2