  The Visual-Dialog Based Emotion Explanation Generation Challenge focuses on
generating emotion explanations through visual-dialog interactions in art
discussions. Our approach combines state-of-the-art multi-modal models,
including Language Model (LM) and Large Vision Language Model (LVLM), to
achieve superior performance. By leveraging these models, we outperform
existing benchmarks, securing the top rank in the ICCV23 Visual-Dialog Based
Emotion Explanation Generation Challenge, which is part of the 5th Workshop On
Closing The Loop Between Vision And Language (CLCV) with significant scores in
F1 and BLEU metrics. Our method demonstrates exceptional ability in generating
accurate emotion explanations, advancing our understanding of emotional impacts
in art.
