Second, we design a novel regression loss, i.e. 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. 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. In this paper, we study the fine-grained regression problem of visual emotions based on convolutional neural networks (CNNs). ![]() However, these methods cannot well reflect the complexity and subtlety of emotions. ![]() assigning an image with a dominant discrete emotion category. ![]() Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e.
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