  "How does the person in the bounding box feel?" Achieving human-level
recognition of the apparent emotion of a person in real world situations
remains an unsolved task in computer vision. Facial expressions are not enough:
body pose, contextual knowledge, and commonsense reasoning all contribute to
how humans perform this emotional theory of mind task. In this paper, we
examine two major approaches enabled by recent large vision language models: 1)
image captioning followed by a language-only LLM, and 2) vision language
models, under zero-shot and fine-tuned setups. We evaluate the methods on the
Emotions in Context (EMOTIC) dataset and demonstrate that a vision language
model, fine-tuned even on a small dataset, can significantly outperform
traditional baselines. The results of this work aim to help robots and agents
perform emotionally sensitive decision-making and interaction in the future.
