Summary of Iccv23 Visual-dialog Emotion Explanation Challenge: Seu_309 Team Technical Report, by Yixiao Yuan and Yingzhe Peng
ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team Technical Report
by Yixiao Yuan, Yingzhe Peng
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel challenge in emotion explanation generation is introduced, focusing on visual-dialog interactions in art discussions. To excel in this task, a multi-modal approach combining state-of-the-art Language Model (LM) and Large Vision Language Model (LVLM) models is employed. This hybrid model outperforms existing benchmarks, achieving the top rank in the ICCV23 Visual-Dialog Based Emotion Explanation Generation Challenge with notable F1 and BLEU scores. By generating accurate emotion explanations, this method advances our understanding of emotional impacts in art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to understand emotions in artwork by talking about it and showing pictures. It’s like having a conversation with an artist, but instead of saying “I love this painting,” the computer generates words that explain why someone might feel a certain way when looking at the artwork. The computer uses special models that combine language and vision to do this. It’s really good at it too, beating other computer systems in a competition. |
Keywords
» Artificial intelligence » Bleu » Language model » Multi modal
