  Context-aware emotion recognition (CAER) is a complex and significant task
that requires perceiving emotions from various contextual cues. Previous
approaches primarily focus on designing sophisticated architectures to extract
emotional cues from images. However, their knowledge is confined to specific
training datasets and may reflect the subjective emotional biases of the
annotators. Furthermore, acquiring large amounts of labeled data is often
challenging in real-world applications. In this paper, we systematically
explore the potential of leveraging Large Vision-Language Models (LVLMs) to
empower the CAER task from three paradigms: 1) We fine-tune LVLMs on two CAER
datasets, which is the most common way to transfer large models to downstream
tasks. 2) We design zero-shot and few-shot patterns to evaluate the performance
of LVLMs in scenarios with limited data or even completely unseen. In this
case, a training-free framework is proposed to fully exploit the In-Context
Learning (ICL) capabilities of LVLMs. Specifically, we develop an image
similarity-based ranking algorithm to retrieve examples; subsequently, the
instructions, retrieved examples, and the test example are combined to feed
LVLMs to obtain the corresponding sentiment judgment. 3) To leverage the rich
knowledge base of LVLMs, we incorporate Chain-of-Thought (CoT) into our
framework to enhance the model's reasoning ability and provide interpretable
results. Extensive experiments and analyses demonstrate that LVLMs achieve
competitive performance in the CAER task across different paradigms. Notably,
the superior performance in few-shot settings indicates the feasibility of
LVLMs for accomplishing specific tasks without extensive training.
