343 research outputs found
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
Visual multimedia have become an inseparable part of our digital social
lives, and they often capture moments tied with deep affections. Automated
visual sentiment analysis tools can provide a means of extracting the rich
feelings and latent dispositions embedded in these media. In this work, we
explore how Convolutional Neural Networks (CNNs), a now de facto computational
machine learning tool particularly in the area of Computer Vision, can be
specifically applied to the task of visual sentiment prediction. We accomplish
this through fine-tuning experiments using a state-of-the-art CNN and via
rigorous architecture analysis, we present several modifications that lead to
accuracy improvements over prior art on a dataset of images from a popular
social media platform. We additionally present visualizations of local patterns
that the network learned to associate with image sentiment for insight into how
visual positivity (or negativity) is perceived by the model.Comment: Accepted for publication in Image and Vision Computing. Models and
source code available at https://github.com/imatge-upc/sentiment-201
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Existing methods on visual emotion analysis mainly focus on coarse-grained
emotion classification, i.e. assigning an image with a dominant discrete
emotion category. However, these methods cannot well reflect the complexity and
subtlety of emotions. In this paper, we study the fine-grained regression
problem of visual emotions based on convolutional neural networks (CNNs).
Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet),
a novel network architecture that integrates attention into a CNN with an
emotion polarity constraint. First, we propose to incorporate both spatial and
channel-wise attentions into a CNN for visual emotion regression, which jointly
considers the local spatial connectivity patterns along each channel and the
interdependency between different channels. Second, we design a novel
regression loss, i.e. polarity-consistent regression (PCR) loss, based on the
weakly supervised emotion polarity to guide the attention generation. By
optimizing the PCR loss, PDANet can generate a polarity preserved attention map
and thus improve the emotion regression performance. Extensive experiments are
conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate
that the proposed PDANet outperforms the state-of-the-art approaches by a large
margin for fine-grained visual emotion regression. Our source code is released
at: https://github.com/ZizhouJia/PDANet.Comment: Accepted by ACM Multimedia 201
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
Emoji’s sentiment score estimation using convolutional neural network with multi-scale emoji images
Emojis are any small images, symbols, or icons that are used in social media. Several well-known emojis have been ranked and sentiment scores have been assigned to them. These ranked emojis can be used for sentiment analysis; however, many new released emojis have not been ranked and have no sentiment score yet. This paper proposes a new method to estimate the sentiment score of any unranked emotion emoji from its image by classifying it into the class of the most similar ranked emoji and then estimating the sentiment score using the score of the most similar emoji. The accuracy of sentiment score estimation is improved by using multi-scale images. The ranked emoji image data set consisted of 613 classes with 161 emoji images from three different platforms in each class. The images were cropped to produce multi-scale images. The classification and estimation were performed by using convolutional neural network (CNN) with multi-scale emoji images and the proposed voting algorithm called the majority voting with probability (MVP). The proposed method was evaluated on two datasets: ranked emoji images and unranked emoji images. The accuracies of sentiment score estimation for the ranked and unranked emoji test images are 98% and 51%, respectively
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