[2604.08641] On Semiotic-Grounded Interpretive Evaluation of Generative Art

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.08641 (cs) [Submitted on 9 Apr 2026] Title:On Semiotic-Grounded Interpretive Evaluation of Generative Art Authors:Ruixiang Jiang, Changwen Chen View a PDF of the paper titled On Semiotic-Grounded Interpretive Evaluation of Generative Art, by Ruixiang Jiang and 1 other authors View PDF HTML (experimental) Abstract: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-i...