用绘画内容与风格双维度自动评估创造力，结果可解释且接近人工评分。
Simple Lines, Big Ideas: Towards Interpretable Assessment of Human Creativity from Drawings
- 基于内容与风格双维度建模，融合语义与笔触特征。
- 在标注数据上实现比现有方法更高的评分预测准确率。
- 可视化结果与人类判断高度一致，适合心理学与教育研究使用。
通过视觉输出（如绘画）评估人类创造力在心理学、教育学和认知科学中具有重要意义。然而，当前评估仍严重依赖专家主观打分，耗时且主观性强。本文提出一种数据驱动的自动可解释创造力评估框架。受[6]中认知证据启发，创造力源于“画什么”（内容）与“怎么画”（风格）的结合，我们将创意分数重新定义为这两类互补维度的函数。首先，我们在已有标注数据集基础上，补充内容类别标注；随后，提出一个联合预测内容、风格与评分的条件模型。该模型通过动态调整视觉特征提取，根据绘画的风格与语义线索自适应聚焦创造力相关信号。实验表明，该模型在预测性能上优于现有回归方法，并生成与人类判断高度一致的可解释可视化结果。代码与标注将公开于 https://github.com/WonderOfU9/CSCA_PRCV_2025。
原文摘要 · Abstract (English)
Assessing human creativity through visual outputs, such as drawings, plays a critical role in fields including psychology, education, and cognitive science. However, current assessment practices still rely heavily on expert-based subjective scoring, which is both labor-intensive and inherently subjective. In this paper, we propose a data-driven framework for automatic and interpretable creativity assessment from drawings. Motivated by the cognitive evidence proposed in [6] that creativity can emerge from both what is drawn (content) and how it is drawn (style), we reinterpret the creativity score as a function of these two complementary dimensions. Specifically, we first augment an existing creativity-labeled dataset with additional annotations targeting content categories. Based on the enriched dataset, we further propose a conditional model predicting content, style, and ratings simultaneously. In particular, the conditional learning mechanism that enables the model to adapt its visual feature extraction by dynamically tuning it to creativity-relevant signals conditioned on the drawing's stylistic and semantic cues. Experimental results demonstrate that our model achieves state-of-the-art performance compared to existing regression-based approaches and offers interpretable visualizations that align well with human judgments. The code and annotations will be made publicly available at https://github.com/WonderOfU9/CSCA_PRCV_2025
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。