用符号学理论评估生成艺术的深层意义，让机器懂艺术隐喻。
On Semiotic-Grounded Interpretive Evaluation of Generative Art
- 基于皮尔斯符号学构建层级意义图，解析生成艺术中的象征与指代关系。
- 在细粒度艺术评价数据集上，其判断与人类一致率显著高于现有方法。
- 适合关注艺术表达深度、追求人文价值的生成艺术研究者使用。
解释是破译艺术语言的关键：观众通过视觉作品解读创作者意图。然而，当前生成艺术（GenArt）评估仍局限于图像质量或提示词匹配，无法衡量创作者意图的深层象征或抽象意义。本文提出一种基于皮尔斯符号学的计算框架，将人-生成艺术互动（HGI）建模为连续的符号化进程。该框架揭示艺术意义通过三类模式传递：象形、象征与指示，但现有评估多局限于象形模式，对后两者结构上盲视。为此，我们提出SemJudge，通过层次化符号图（HSG）重构从提示到生成作品的意义生成过程，显式评估象征与指示意义。大量定量实验表明，在强调解释性的精细艺术基准上，SemJudge与人类判断更一致；用户研究进一步证明其能生成更深入、更有洞察力的艺术解读。这为生成艺术从‘美观图像’迈向表达复杂人类经验的新媒介铺平道路。项目页面：https://github.com/songrise/SemJudge。
原文摘要 · Abstract (English)
Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. However, current Generative Art (GenArt) evaluators remain fixated on surface-level image quality or literal prompt adherence, failing to assess the deeper symbolic or abstract meaning intended by the creator. We address this gap by formalizing a Peircean computational semiotic theory that models Human-GenArt Interaction (HGI) as cascaded semiosis. This framework reveals that artistic meaning is conveyed through three modes - iconic, symbolic, and indexical - yet existing evaluators operate heavily within the iconic mode, remaining structurally blind to the latter two. To overcome this structural blindness, we propose SemJudge. This evaluator explicitly assesses symbolic and indexical meaning in HGI via a Hierarchical Semiosis Graph (HSG) that reconstructs the meaning-making process from prompt to generated artifact. Extensive quantitative experiments show that SemJudge aligns more closely with human judgments than prior evaluators on an interpretation-intensive fine-art benchmark. User studies further demonstrate that SemJudge produces deeper, more insightful artistic interpretations, thereby paving the way for GenArt to move beyond the generation of "pretty" images toward a medium capable of expressing complex human experience. Project page: https://github.com/songrise/SemJudge.
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