Attributes-aware Visual Emotion Representation Learning (2504.06578v1)

Abstract: Visual emotion analysis or recognition has gained considerable attention due to the growing interest in understanding how images can convey rich semantics and evoke emotions in human perception. However, visual emotion analysis poses distinctive challenges compared to traditional vision tasks, especially due to the intricate relationship between general visual features and the different affective states they evoke, known as the affective gap. Researchers have used deep representation learning methods to address this challenge of extracting generalized features from entire images. However, most existing methods overlook the importance of specific emotional attributes such as brightness, colorfulness, scene understanding, and facial expressions. Through this paper, we introduce A4Net, a deep representation network to bridge the affective gap by leveraging four key attributes: brightness (Attribute 1), colorfulness (Attribute 2), scene context (Attribute 3), and facial expressions (Attribute 4). By fusing and jointly training all aspects of attribute recognition and visual emotion analysis, A4Net aims to provide a better insight into emotional content in images. Experimental results show the effectiveness of A4Net, showcasing competitive performance compared to state-of-the-art methods across diverse visual emotion datasets. Furthermore, visualizations of activation maps generated by A4Net offer insights into its ability to generalize across different visual emotion datasets.

Attributes-aware Visual Emotion Representation Learning

The paper "Attributes-aware Visual Emotion Representation Learning" introduces an innovative approach to visual emotion analysis by emphasizing the significance of specific emotional attributes often overlooked by traditional methods. Visual emotion analysis is complex due to the affective gap—the disconnect between general visual features and the emotional states they evoke. To bridge this gap, the authors propose A4Net, a deep representation network that integrates four key attributes: brightness, colorfulness, scene context, and facial expressions.

Overview of A4Net and Methodology

A4Net addresses visual emotion analysis by employing a multi-label classification and regression framework with dedicated branches for each attribute. The backbone of A4Net is based on ConvNeXt-V2, which facilitates the learning of rich feature vectors that represent brightness, colorfulness, scene context, and facial expressions individually. These specialized branches extract features that serve as inputs for the emotion classifier, enabling a more nuanced understanding of emotional content in images.

Attribute-specific branches encompass:

1. Brightness and Colorfulness Estimation: These branches use feature vectors extracted from early layers of the backbone network to perform regression tasks on brightness and colorfulness values, which are crucial in perceptual processing and evoking emotional responses.
2. Scene Recognition: Utilizing global scene recognition strategies, the network effectively categorizes images into specific scene types, highlighting the role of contextual elements in emotional evocation.
3. Facial Expression Recognition: This branch capitalizes on facial expression detection without pre-processing the images, leveraging the inherent expressiveness of human faces for emotional perception.

The combined output from these branches is integrated into the visual emotion classifier, which predicts the emotional class by analyzing fused feature vectors, managed by trainable parameters to prioritize certain attributes.

Experimental Evaluation and Comparative Analysis

The paper conducts extensive experiments on several datasets—EmoSet, EMOTIC, SE30K8, and UnBiasEmo—demonstrating that A4Net excels in visual emotion recognition tasks. The performance of A4Net is superior to traditional convolutional networks and existing state-of-the-art methods. On EMOTIC and UnBiasEmo, A4Net particularly showcases enhanced accuracy and generalization capability, reflecting its adeptness in capturing diverse emotional features.

Implications and Future Directions

A4Net’s methodology underscores the critical role of attribute-aware learning in overcoming the affective gap in visual emotion analysis. The effectiveness of integrating multiple visual cues highlights potential applications across behavioral sciences, mental health assessments, marketing, and entertainment industries.

Future research can explore the integration of additional attributes such as human activities and object characteristics, enhancing emotion representation. Investigating the permutational relationship between these attributes to augment emotion recognition further poses an intriguing challenge. Additionally, adapting these methods to abstract imagery may offer new insights into diverse emotional responses beyond natural scenes.

Conclusion

The paper introduces a paradigm shift in visual emotion representation learning by presenting A4Net, with its ability to leverage distinct attributes for bridging the affective gap. The development and outcomes associated with A4Net pave the way for expanded research in AI-driven emotion analysis, emphasizing the exploration of attributes in shaping human emotional perception.