Title: HugMe: Multi-view Emotion Learning on Heterogeneous Graph
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Emotion recognition, context-aware, multi-view

Abstract: Human emotions are the cornerstone of social interaction. Empowering machine vision with emotion perception is crucial for building harmonious and empathetic human-machine collaborative systems across a wide range of domains. Most mainstream approaches, based on a given image, adhere to the traditional image content understanding paradigm and conduct end-to-end emotion learning from
the perspective of semantics-emotion association. Despite significant progress, several challenges remain. From the perspective of visual information representation, existing methods mostly adhere to the traditional image content semantics understanding paradigm and conduct end-to-end emotion learning from the perspective of content semantics-emotion semantics association, neglecting the rep-resentation and utilization of the rich structural information inherent in images. From the perspective of label information representation, existing methods mostly either directly map and classify visual features using a one-hot labeling approach, resulting in human emotion labels being treated as meaningless label indices; or they simply establish single associations between emotion labels. The heterogeneous association patterns inherent in complex human emotions have been largely unexplored. To this end, in this paper, we propose a novel HugMe model by Multi-view emotion learning on Heterogeneous graph. Specifically, for visual feature learning, we first develop a multi-view emotion representation method to leverage rich visual features from the perspectives of both semantics and structures. For label feature learning, we propose a heterogeneous emotion graph representation approach, which leverages heterogeneous graph to model the complex and diverse association patterns between different emotional labels. Finally, we develop a multi-view emotion classification module to better recognize different emotions for the given person in the image. In addition to the traditional classification loss function, to better learn and optimize our proposed HugMe, we also design a double-constraint loss function to supervise the label learning process. Extensive experiment results on well-studied human emotion benchmark datasets demonstrate the superiority and rationality of HugMe.