摘要：图像与表格数据的多模态学习往往受到无效表示的影响，导致冗余、离散和泛化问题。为应对这一挑战，我们提出图增强描述符排序（Graph-Enhanced Descriptor Sequencing，GEDS），一种以列排列问题（Column Permutation Problem，CPP）原理为基础的结构化特征排序算法。GEDS 通过基于相似度图的计算来优化特征的统计描述符，并系统地确定有效的特征排序。我们将 GEDS 整合到一个具备顺序感知能力的高效 Transformer 框架中，使用顺序感知内存 token，并通过专用损失函数使其明确遵循所推导出的特征顺序。多模态基准测试结果表明，iStructTab 能够有效减少特征离散度，提升预测性能和鲁棒性，并凸显结构化特征排序在多模态学习中的重要意义。

Abstract:Multimodal learning of images and tabular data is often impaired by ineffective representations, resulting in redundancy, dispersion, and generalization problems. To tackle this challenge, we introduce Graph-Enhanced Descriptor Sequencing (GEDS), a structured feature sequencing algorithm grounded in principles from the Column Permutation Problem (CPP). GEDS refines statistical descriptors of the features through similarity graph-based computations, systematically determining an effective feature sequencing. We incorporate GEDS within an order-aware efficient transformer framework, utilizing order-aware memory tokens that explicitly adhere to the derived feature sequencing via a dedicated loss function. Experimental results across multimodal benchmarks demonstrate that iStructTab effectively minimizes feature dispersion, improving predictive performance and robustness, and highlighting the significance of structured feature sequencing in multimodal learning.