14 research outputs found
Quantifying Facial Age by Posterior of Age Comparisons
We introduce a novel approach for annotating large quantity of in-the-wild
facial images with high-quality posterior age distribution as labels. Each
posterior provides a probability distribution of estimated ages for a face. Our
approach is motivated by observations that it is easier to distinguish who is
the older of two people than to determine the person's actual age. Given a
reference database with samples of known ages and a dataset to label, we can
transfer reliable annotations from the former to the latter via
human-in-the-loop comparisons. We show an effective way to transform such
comparisons to posterior via fully-connected and SoftMax layers, so as to
permit end-to-end training in a deep network. Thanks to the efficient and
effective annotation approach, we collect a new large-scale facial age dataset,
dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from
our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and
github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a
network that jointly performs ordinal hyperplane classification and posterior
distribution learning. Our approach achieves state-of-the-art results on
popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio
Some like it hot - visual guidance for preference prediction
For people first impressions of someone are of determining importance. They
are hard to alter through further information. This begs the question if a
computer can reach the same judgement. Earlier research has already pointed out
that age, gender, and average attractiveness can be estimated with reasonable
precision. We improve the state-of-the-art, but also predict - based on
someone's known preferences - how much that particular person is attracted to a
novel face. Our computational pipeline comprises a face detector, convolutional
neural networks for the extraction of deep features, standard support vector
regression for gender, age and facial beauty, and - as the main novelties -
visual regularized collaborative filtering to infer inter-person preferences as
well as a novel regression technique for handling visual queries without rating
history. We validate the method using a very large dataset from a dating site
as well as images from celebrities. Our experiments yield convincing results,
i.e. we predict 76% of the ratings correctly solely based on an image, and
reveal some sociologically relevant conclusions. We also validate our
collaborative filtering solution on the standard MovieLens rating dataset,
augmented with movie posters, to predict an individual's movie rating. We
demonstrate our algorithms on howhot.io which went viral around the Internet
with more than 50 million pictures evaluated in the first month.Comment: accepted for publication at CVPR 201
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
We show a Talagrand-type concentration inequality for Multi-Task Learning
(MTL), using which we establish sharp excess risk bounds for MTL in terms of
distribution- and data-dependent versions of the Local Rademacher Complexity
(LRC). We also give a new bound on the LRC for norm regularized as well as
strongly convex hypothesis classes, which applies not only to MTL but also to
the standard i.i.d. setting. Combining both results, one can now easily derive
fast-rate bounds on the excess risk for many prominent MTL methods,
including---as we demonstrate---Schatten-norm, group-norm, and
graph-regularized MTL. The derived bounds reflect a relationship akeen to a
conservation law of asymptotic convergence rates. This very relationship allows
for trading off slower rates w.r.t. the number of tasks for faster rates with
respect to the number of available samples per task, when compared to the rates
obtained via a traditional, global Rademacher analysis.Comment: In this version, some arguments and results (of the previous version)
have been corrected, or modifie