EgoNormia: Benchmarking Physical Social Norm Understanding
Authors: name: MohammadHossein Rezaei, name: Yicheng Fu, name: Phil Cuvin, name: Caleb Ziems, name: Yanzhe Zhang, name: Hao Zhu, name: Diyi Yang

Human activity is moderated by norms. When performing actions in the real
world, humans not only follow norms, but also consider the trade-off between
different norms However, machines are often trained without explicit
supervision on norm understanding and reasoning, especially when the norms are
grounded in a physical and social context. To improve and evaluate the
normative reasoning capability of vision-language models (VLMs), we present
EgoNormia |epsilon|, consisting of 1,853 ego-centric videos of human
interactions, each of which has two related questions evaluating both the
prediction and justification of normative actions. The normative actions
encompass seven categories: safety, privacy, proxemics, politeness,
cooperation, coordination/proactivity, and communication/legibility. To compile
this dataset at scale, we propose a novel pipeline leveraging video sampling,
automatic answer generation, filtering, and human validation. Our work
demonstrates that current state-of-the-art vision-language models lack robust
norm understanding, scoring a maximum of 45% on EgoNormia (versus a human bench
of 92%). Our analysis of performance in each dimension highlights the
significant risks of safety, privacy, and the lack of collaboration and
communication capability when applied to real-world agents. We additionally
show that through a retrieval-based generation method, it is possible to use
EgoNomia to enhance normative reasoning in VLMs.