ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team Technical Report
<|reference_start|>ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team Technical Report: 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.<|reference_end|>
@article{yuan2024iccv23,
    title={ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team
  Technical Report},
    author={Yixiao Yuan and Yingzhe Peng},
    journal={arXiv preprint arXiv:2407.09760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09760},
    primaryClass={cs.CV cs.AI}
}