Combinational creativity, a form of creativity involving the blending of familiar ideas, is pivotal in design innovation. While most research focuses on how combinational creativity in design is achieved through blending elements, this study focuses on the computational interpretation, specifically identifying the 'base' and 'additive' components that constitute a creative design. To achieve this goal, the authors propose a heuristic algorithm integrating computer vision and natural language processing technologies, and implement multiple approaches based on both discriminative and generative artificial intelligence architectures. A comprehensive evaluation was conducted on a dataset created for studying combinational creativity. Among the implementations of the proposed algorithm, the most effective approach demonstrated a high accuracy in interpretation, achieving 87.5% for identifying 'base' and 80% for 'additive'. We conduct a modular analysis and an ablation experiment to assess the performance of each part in our implementations. Additionally, the study includes an analysis of error cases and bottleneck issues, providing critical insights into the limitations and challenges inherent in the computational interpretation of creative designs.

翻译：组合创造力，一种涉及融合熟悉观念的创造力形式，在设计创新中至关重要。尽管多数研究聚焦于设计中的组合创造力如何通过元素融合实现，本研究则关注计算解释，特别是识别构成创意设计的“基础”与“附加”成分。为实现此目标，作者提出了一种集成计算机视觉与自然语言处理技术的启发式算法，并基于判别式与生成式人工智能架构实施了多种方法。在一个专为研究组合创造力创建的数据集上进行了全面评估。在提出算法的各类实现中，最有效的方法在解释上展现出高准确率，对“基础”成分的识别准确率达87.5%，对“附加”成分的识别准确率达80%。我们通过模块化分析与消融实验评估了实现方案中各部分的性能。此外，研究还包含误差案例与瓶颈问题分析，为创造性设计计算解释中的固有限制与挑战提供了关键见解。