3,741 research outputs found
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Psychological research results have confirmed that people can have different
emotional reactions to different visual stimuli. Several papers have been
published on the problem of visual emotion analysis. In particular, attempts
have been made to analyze and predict people's emotional reaction towards
images. To this end, different kinds of hand-tuned features are proposed. The
results reported on several carefully selected and labeled small image data
sets have confirmed the promise of such features. While the recent successes of
many computer vision related tasks are due to the adoption of Convolutional
Neural Networks (CNNs), visual emotion analysis has not achieved the same level
of success. This may be primarily due to the unavailability of confidently
labeled and relatively large image data sets for visual emotion analysis. In
this work, we introduce a new data set, which started from 3+ million weakly
labeled images of different emotions and ended up 30 times as large as the
current largest publicly available visual emotion data set. We hope that this
data set encourages further research on visual emotion analysis. We also
perform extensive benchmarking analyses on this large data set using the state
of the art methods including CNNs.Comment: 7 pages, 7 figures, AAAI 201
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction
Visual media are powerful means of expressing emotions and sentiments. The
constant generation of new content in social networks highlights the need of
automated visual sentiment analysis tools. While Convolutional Neural Networks
(CNNs) have established a new state-of-the-art in several vision problems,
their application to the task of sentiment analysis is mostly unexplored and
there are few studies regarding how to design CNNs for this purpose. In this
work, we study the suitability of fine-tuning a CNN for visual sentiment
prediction as well as explore performance boosting techniques within this deep
learning setting. Finally, we provide a deep-dive analysis into a benchmark,
state-of-the-art network architecture to gain insight about how to design
patterns for CNNs on the task of visual sentiment prediction.Comment: Preprint of the paper accepted at the 1st Workshop on Affect and
Sentiment in Multimedia (ASM), in ACM MultiMedia 2015. Brisbane, Australi
More cat than cute? Interpretable Prediction of Adjective-Noun Pairs
The increasing availability of affect-rich multimedia resources has bolstered
interest in understanding sentiment and emotions in and from visual content.
Adjective-noun pairs (ANP) are a popular mid-level semantic construct for
capturing affect via visually detectable concepts such as "cute dog" or
"beautiful landscape". Current state-of-the-art methods approach ANP prediction
by considering each of these compound concepts as individual tokens, ignoring
the underlying relationships in ANPs. This work aims at disentangling the
contributions of the `adjectives' and `nouns' in the visual prediction of ANPs.
Two specialised classifiers, one trained for detecting adjectives and another
for nouns, are fused to predict 553 different ANPs. The resulting ANP
prediction model is more interpretable as it allows us to study contributions
of the adjective and noun components. Source code and models are available at
https://imatge-upc.github.io/affective-2017-musa2/ .Comment: Oral paper at ACM Multimedia 2017 Workshop on Multimodal
Understanding of Social, Affective and Subjective Attributes (MUSA2
What your Facebook Profile Picture Reveals about your Personality
People spend considerable effort managing the impressions they give others.
Social psychologists have shown that people manage these impressions
differently depending upon their personality. Facebook and other social media
provide a new forum for this fundamental process; hence, understanding people's
behaviour on social media could provide interesting insights on their
personality. In this paper we investigate automatic personality recognition
from Facebook profile pictures. We analyze the effectiveness of four families
of visual features and we discuss some human interpretable patterns that
explain the personality traits of the individuals. For example, extroverts and
agreeable individuals tend to have warm colored pictures and to exhibit many
faces in their portraits, mirroring their inclination to socialize; while
neurotic ones have a prevalence of pictures of indoor places. Then, we propose
a classification approach to automatically recognize personality traits from
these visual features. Finally, we compare the performance of our
classification approach to the one obtained by human raters and we show that
computer-based classifications are significantly more accurate than averaged
human-based classifications for Extraversion and Neuroticism
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