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English(EN) Beyond Vision-Language Models: Visual-Semantic Affect Graphs for Emotion Recognition in Artworks

新图框架增强艺术品情感识别能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了视觉-语义情感图（VSAG），一种新颖的基于图的框架，用于识别艺术品中的情感。该方法通过表示可解释的特征节点（如颜色、笔触和语义关联）来对艺术品进行建模，以预测主导情感。VSAG框架，特别是其分层效价-唤醒变体，在EmoArt-5k数据集上，与直接从Qwen3-VL和InternVL3.5等视觉-语言模型进行推理相比，表现出更优越的性能，这表明显式的视觉-语义结构增强了艺术情感识别中的预测和解释能力。 AI

影响 引入了一种新颖的基于图的方法，可以提高多模态AI解释复杂数据中情感等主观内容的能力。

排序理由 介绍艺术品情感识别新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新图框架增强艺术品情感识别能力

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介绍艺术品情感识别新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ngoc-Duy Mai, Ha-Thanh Nguyen, Hideaki Takeda ·

    超越视觉语言模型：用于艺术品情感识别的视觉-语义情感图

    arXiv:2610.35244v1 Announce Type: cross Abstract: Emotion recognition in artworks remains challenging because affective meaning is often subjective, implicit, and shaped by relationships between visual form and semantic associations. While vision-language models provide strong mu…

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