VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models

VidEmo presents a novel approach to emotion understanding in videos by leveraging advancements in video large language models. This method specifically addresses the dynamic nature of emotions, recognizing that emotional expressions in video content are complex and influenced by multiple cues over time. By focusing on the affective aspects, VidEmo aims to improve the accuracy and depth of emotion-centric analysis within video foundation models. The approach reflects ongoing research efforts to enhance machine understanding of human emotions in multimedia contexts, as documented in recent studies on arXiv. VidEmo’s contribution lies in its ability to integrate temporal and contextual information, which is crucial for capturing the fluidity of emotional states. This development marks a step forward in the field of computer vision and affective computing, providing a foundation for more nuanced emotion recognition systems.