2,816 research outputs found
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202
CAKE: Compact and Accurate K-dimensional representation of Emotion
Numerous models describing the human emotional states have been built by the
psychology community. Alongside, Deep Neural Networks (DNN) are reaching
excellent performances and are becoming interesting features extraction tools
in many computer vision tasks.Inspired by works from the psychology community,
we first study the link between the compact two-dimensional representation of
the emotion known as arousal-valence, and discrete emotion classes (e.g. anger,
happiness, sadness, etc.) used in the computer vision community. It enables to
assess the benefits -- in terms of discrete emotion inference -- of adding an
extra dimension to arousal-valence (usually named dominance). Building on these
observations, we propose CAKE, a 3-dimensional representation of emotion
learned in a multi-domain fashion, achieving accurate emotion recognition on
several public datasets. Moreover, we visualize how emotions boundaries are
organized inside DNN representations and show that DNNs are implicitly learning
arousal-valence-like descriptions of emotions. Finally, we use the CAKE
representation to compare the quality of the annotations of different public
datasets
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