382,883 research outputs found
Understanding Image Virality
Virality of online content on social networking websites is an important but
esoteric phenomenon often studied in fields like marketing, psychology and data
mining. In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score
using Reddit metadata. We train classifiers with state-of-the-art image
features to predict virality of individual images, relative virality in pairs
of images, and the dominant topic of a viral image. We also compare machine
performance to human performance on these tasks. We find that computers perform
poorly with low level features, and high level information is critical for
predicting virality. We encode semantic information through relative
attributes. We identify the 5 key visual attributes that correlate with
virality. We create an attribute-based characterization of images that can
predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes)
-- better than humans at 60.12%. Finally, we study how human prediction of
image virality varies with different `contexts' in which the images are viewed,
such as the influence of neighbouring images, images recently viewed, as well
as the image title or caption. This work is a first step in understanding the
complex but important phenomenon of image virality. Our datasets and
annotations will be made publicly available.Comment: Pre-print, IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
End-to-End Localization and Ranking for Relative Attributes
We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201
Retrieving relative soft biometrics for semantic identification
Automatically describing pedestrians in surveillance footage is crucial to facilitate human accessible solutions for suspect identification. We aim to identify pedestrians based solely on human description, by automatically retrieving semantic attributes from surveillance images, alleviating exhaustive label annotation. This work unites a deep learning solution with relative soft biometric labels, to accurately retrieve more discriminative image attributes. We propose a Semantic Retrieval Convolutional Neural Network to investigate automatic retrieval of three soft biometric modalities, across a number of 'closed-world' and 'open-world' re-identification scenarios. Findings suggest that relative-continuous labels are more accurately predicted than absolute-binary and relative-binary labels, improving semantic identification in every scenario. Furthermore, we demonstrate a top rank-1 improvement of 23.2% and 26.3% over a traditional, baseline retrieval approach, in one-shot and multi-shot re-identification scenarios respectively
The Gender Wage Gap in Portugal: Recent Evolution and Decomposition
Using data from the Personnel Records (Quadros de Pessoal) for the period 1985-2000, we analyse the gender wage gap in Portugal. We estimate wage discrimination and endowment differentials using four decomposition methods. Our main concern is to analyse the key factors that lie behind the persistent gender pay gap despite the deep changes that characterise the recent evolution of the Portuguese labour market and the high female participation rate that exists in the country. Moreover, using the Neumark methodology, we discuss the relative contribution of different factors in explaining the gender pay gap. The results suggest that, in accordance with previous international research, the measured discrimination differential dominates the estimated endowment differential. Over time, a relevant discrimination gap persisted and it didn’t show any tendency to decrease. Results are also consistent in showing that the most important difference in attributes to explain the gender pay gap is the way how males and females are distributed by sector of industry. As to human capital variables, their relative importance to the explanation of the gender pay gap has reduced sharply, particularly along the 90’s.Labour market; discrimination; wage differential; gender
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