Rank-O-ToM: Unlocking Emotional Nuance Ranking to Enhance Affective Theory-of-Mind

Abstract

Facial Expression Recognition (FER) plays a foundational role in enabling AI systems to interpret emotional nuances, a critical aspect of affective Theory of Mind (ToM). However, existing models often struggle with poor calibration and a limited capacity to capture emotional intensity and complexity. To address this, we propose Ranking the Emotional Nuance for Theory of Mind (Rank-O-ToM), a framework that leverages ordinal ranking to align confidence levels with the emotional spectrum. By incorporating synthetic samples reflecting diverse affective complexities, Rank-O-ToM enhances the nuanced understanding of emotions, advancing AI's ability to reason about affective states.

Accepted to AAAI 2025 Theory of Mind for AI (ToM4AI) Workshop (Spotlight) JiHyun Kim, JuneHyoung Kwon, MiHyeon Kim, and Eunju Lee contributed equally as co-first authors. YoungBin Kim is the corresponding author