As machine learning models increase in scale and complexity, obtaining sufficient training data has become a critical bottleneck due to acquisition costs, privacy constraints, and data scarcity in specialised domains. While synthetic data generation has emerged as a promising alternative, a notable performance gap remains compared to models trained on real data, particularly as task complexity grows. Concurrently, Neuro-Symbolic methods, which combine neural networks' learning strengths with symbolic reasoning's structured representations, have demonstrated significant potential across various cognitive tasks. This paper explores the utility of Neuro-Symbolic conditioning for synthetic image dataset generation, focusing specifically on improving the performance of Scene Graph Generation models. The research investigates whether structured symbolic representations in the form of scene graphs can enhance synthetic data quality through explicit encoding of relational constraints. The results demonstrate that Neuro-Symbolic conditioning yields significant improvements of up to +2.59% in standard Recall metrics and +2.83% in No Graph Constraint Recall metrics when used for dataset augmentation. These findings establish that merging Neuro-Symbolic and generative approaches produces synthetic data with complementary structural information that enhances model performance when combined with real data, providing a novel approach to overcome data scarcity limitations even for complex visual reasoning tasks.

翻译：随着机器学习模型规模和复杂度的不断提升，获取充足的训练数据已成为一个关键瓶颈，这主要源于采集成本、隐私约束以及专业领域的数据稀缺性。虽然合成数据生成已成为一种有前景的替代方案，但与在真实数据上训练的模型相比，尤其是在任务复杂度增加时，仍存在显著的性能差距。与此同时，神经符号方法——它结合了神经网络的学习优势与符号推理的结构化表示能力——已在多种认知任务中展现出巨大潜力。本文探讨了神经符号条件化在合成图像数据集生成中的应用，特别关注于提升场景图生成模型的性能。本研究探究了以场景图形式存在的结构化符号表示，是否能够通过对关系约束的显式编码来提升合成数据的质量。结果表明，当用于数据集增强时，神经符号条件化在标准召回率指标上带来了高达+2.59%的显著提升，在无图约束召回率指标上提升了+2.83%。这些发现证实，融合神经符号方法与生成方法能够产生具有互补性结构信息的合成数据，当与真实数据结合时能够提升模型性能，这为克服数据稀缺限制——即使是对于复杂的视觉推理任务——提供了一种新颖的途径。