Zobrazeno 1 - 10
of 116 341
pro vyhledávání: '"BOWYER, A."'
This work explores how human judgement about salient regions of an image can be introduced into deep convolutional neural network (DCNN) training. Traditionally, training of DCNNs is purely data-driven. This often results in learning features of the
Externí odkaz:
http://arxiv.org/abs/2410.16190
Autor:
Bowyer, Don
Publikováno v:
College Music Symposium, 2018 Oct 01. 58(2), 1-2.
Externí odkaz:
https://www.jstor.org/stable/26564900
Autor:
Bingham, Neil
Publikováno v:
Twentieth Century Architecture, 2015 Jan 01(12), 84-97.
Externí odkaz:
http://www.jstor.org/stable/24644458
This paper studies how to synthesize face images of non-existent persons, to create a dataset that allows effective training of face recognition (FR) models. Two important goals are (1) the ability to generate a large number of distinct identities (i
Externí odkaz:
http://arxiv.org/abs/2409.02979
Appearance of a face can be greatly altered by growing a beard and mustache. The facial hairstyles in a pair of images can cause marked changes to the impostor distribution and the genuine distribution. Also, different distributions of facial hairsty
Externí odkaz:
http://arxiv.org/abs/2405.20062
Autor:
Wu, Haiyu, Tian, Sicong, Bhatta, Aman, Gutierrez, Jacob, Bezold, Grace, Argueta, Genesis, Ricanek Jr., Karl, King, Michael C., Bowyer, Kevin W.
Face Recognition models are commonly trained with web-scraped datasets containing millions of images and evaluated on test sets emphasizing pose, age and mixed attributes. With train and test sets both assembled from web-scraped images, it is critica
Externí odkaz:
http://arxiv.org/abs/2405.15965
A fundamental tenet of pattern recognition is that overlap between training and testing sets causes an optimistic accuracy estimate. Deep CNNs for face recognition are trained for N-way classification of the identities in the training set. Accuracy i
Externí odkaz:
http://arxiv.org/abs/2405.09403