  Visual Emotion Analysis (VEA) has attracted increasing attention recently
with the prevalence of sharing images on social networks. Since human emotions
are ambiguous and subjective, it is more reasonable to address VEA in a label
distribution learning (LDL) paradigm rather than a single-label classification
task. Different from other LDL tasks, there exist intrinsic relationships
between emotions and unique characteristics within them, as demonstrated in
psychological theories. Inspired by this, we propose a well-grounded
circular-structured representation to utilize the prior knowledge for visual
emotion distribution learning. To be specific, we first construct an Emotion
Circle to unify any emotional state within it. On the proposed Emotion Circle,
each emotion distribution is represented with an emotion vector, which is
defined with three attributes (i.e., emotion polarity, emotion type, emotion
intensity) as well as two properties (i.e., similarity, additivity). Besides,
we design a novel Progressive Circular (PC) loss to penalize the
dissimilarities between predicted emotion vector and labeled one in a
coarse-to-fine manner, which further boosts the learning process in an
emotion-specific way. Extensive experiments and comparisons are conducted on
public visual emotion distribution datasets, and the results demonstrate that
the proposed method outperforms the state-of-the-art methods.
