Attributes-aware Visual Emotion Representation Learning

2025-04-09

AI Summary

Visual sentiment analysis suffers from the “affective gap,” wherein generic visual features inadequately capture subjective emotional states. Existing approaches often neglect interpretable affective attributes—such as brightness, color richness, scene context, and facial expressions—resulting in coarse-grained representations with limited attribution capability. To address this, we propose A4Net: the first end-to-end trainable framework that systematically jointly models these four critical affective attributes. A4Net integrates multi-task learning, attribute-level feature interaction, and attention mechanisms to enable holistic and discriminative sentiment representation. It achieves state-of-the-art performance across multiple benchmark datasets. Activation map visualization demonstrates that A4Net precisely localizes and synergistically responds to each attribute, thereby significantly enhancing cross-domain generalization and decision interpretability.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Bridging the affective gap in visual emotion analysis
• Incorporating emotional attributes like brightness and colorfulness
• Improving emotion recognition via multi-attribute fusion (A4Net)

Innovation

• Leverages brightness, colorfulness, scene, facial attributes
• Fuses joint training for emotion and attribute recognition
• A4Net generalizes across diverse emotion datasets