  Multimodal sentiment analysis has gained significant attention due to the
proliferation of multimodal content on social media. However, existing studies
in this area rely heavily on large-scale supervised data, which is
time-consuming and labor-intensive to collect. Thus, there is a need to address
the challenge of few-shot multimodal sentiment analysis. To tackle this
problem, we propose a novel method called Multimodal Probabilistic Fusion
Prompts (MultiPoint) that leverages diverse cues from different modalities for
multimodal sentiment detection in the few-shot scenario. Specifically, we start
by introducing a Consistently Distributed Sampling approach called CDS, which
ensures that the few-shot dataset has the same category distribution as the
full dataset. Unlike previous approaches primarily using prompts based on the
text modality, we design unified multimodal prompts to reduce discrepancies
between different modalities and dynamically incorporate multimodal
demonstrations into the context of each multimodal instance. To enhance the
model's robustness, we introduce a probabilistic fusion method to fuse output
predictions from multiple diverse prompts for each input. Our extensive
experiments on six datasets demonstrate the effectiveness of our approach.
First, our method outperforms strong baselines in the multimodal few-shot
setting. Furthermore, under the same amount of data (1% of the full dataset),
our CDS-based experimental results significantly outperform those based on
previously sampled datasets constructed from the same number of instances of
each class.
