Tabular foundation model to detect empathy from visual cues

Abstract: Detecting empathy from video interactions is an emerging area of research. Video datasets, however, are often released as extracted features (i.e., tabular data) rather than raw footage due to privacy and ethical concerns. Prior research on such tabular datasets established tree-based classical machine learning approaches as the best-performing models. Motivated by the recent success of textual foundation models (i.e., LLMs), we explore the use of tabular foundation models in empathy detection from tabular visual features. We experiment with two recent tabular foundation models $-$ TabPFN v2 and TabICL $-$ through in-context learning and fine-tuning setups. Our experiments on a public human-robot interaction benchmark demonstrate a significant boost in cross-subject empathy detection accuracy over several strong baselines (accuracy: $0.590 \rightarrow 0.730$; AUC: $0.564 \rightarrow 0.669$). In addition to performance improvement, we contribute novel insights and an evaluation setup to ensure generalisation on unseen subjects in this public benchmark. As the practice of releasing video features as tabular datasets is likely to persist due to privacy constraints, our findings will be widely applicable to future empathy detection video datasets as well.