19,706 research outputs found
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Aesthetic quality prediction is a challenging task in the computer vision
community because of the complex interplay with semantic contents and
photographic technologies. Recent studies on the powerful deep learning based
aesthetic quality assessment usually use a binary high-low label or a numerical
score to represent the aesthetic quality. However the scalar representation
cannot describe well the underlying varieties of the human perception of
aesthetics. In this work, we propose to predict the aesthetic score
distribution (i.e., a score distribution vector of the ordinal basic human
ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs
which aim to minimize the difference between the predicted scalar numbers or
vectors and the ground truth cannot be directly used for the ordinal basic
rating distribution. Thus, a novel CNN based on the Cumulative distribution
with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic
score distribution of human ratings, with a new reliability-sensitive learning
method based on the kurtosis of the score distribution, which eliminates the
requirement of the original full data of human ratings (without normalization).
Experimental results on large scale aesthetic dataset demonstrate the
effectiveness of our introduced CJS-CNN in this task.Comment: AAAI Conference on Artificial Intelligence (AAAI), New Orleans,
Louisiana, USA. 2-7 Feb. 201
What Twitter Profile and Posted Images Reveal About Depression and Anxiety
Previous work has found strong links between the choice of social media
images and users' emotions, demographics and personality traits. In this study,
we examine which attributes of profile and posted images are associated with
depression and anxiety of Twitter users. We used a sample of 28,749 Facebook
users to build a language prediction model of survey-reported depression and
anxiety, and validated it on Twitter on a sample of 887 users who had taken
anxiety and depression surveys. We then applied it to a different set of 4,132
Twitter users to impute language-based depression and anxiety labels, and
extracted interpretable features of posted and profile pictures to uncover the
associations with users' depression and anxiety, controlling for demographics.
For depression, we find that profile pictures suppress positive emotions rather
than display more negative emotions, likely because of social media
self-presentation biases. They also tend to show the single face of the user
(rather than show her in groups of friends), marking increased focus on the
self, emblematic for depression. Posted images are dominated by grayscale and
low aesthetic cohesion across a variety of image features. Profile images of
anxious users are similarly marked by grayscale and low aesthetic cohesion, but
less so than those of depressed users. Finally, we show that image features can
be used to predict depression and anxiety, and that multitask learning that
includes a joint modeling of demographics improves prediction performance.
Overall, we find that the image attributes that mark depression and anxiety
offer a rich lens into these conditions largely congruent with the
psychological literature, and that images on Twitter allow inferences about the
mental health status of users.Comment: ICWSM 201
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