  This report provide a detailed description of the method that we explored and
proposed in the WECIA Emotion Prediction Competition (EPC), which predicts a
person's emotion through an artistic work with a comment. The dataset of this
competition is ArtELingo, designed to encourage work on diversity across
languages and cultures. The dataset has two main challenges, namely modal
imbalance problem and language-cultural differences problem. In order to
address this issue, we propose a simple yet effective approach called
single-multi modal with Emotion-Cultural specific prompt(ECSP), which focuses
on using the single modal message to enhance the performance of multimodal
models and a well-designed prompt to reduce cultural differences problem. To
clarify, our approach contains two main blocks:
(1)XLM-R\cite{conneau2019unsupervised} based unimodal model and
X$^2$-VLM\cite{zeng2022x} based multimodal model (2) Emotion-Cultural specific
prompt. Our approach ranked first in the final test with a score of 0.627.
