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This research paper introduces iStructTab, a novel algorithm using Graph-Enhanced Descriptor Sequencing (GEDS) to optimize feature ordering in multimodal models that combine images and tabular data. By integrating these structured sequences into an order-aware transformer framework, the method effectively reduces redundancy and dispersion while improving predictive robustness on standard benchmarks. The work highlights critical advancements in representation learning for complex datasets where traditional approaches struggle with generalization issues.
iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data
arXiv:2608.04348v1 Announce Type: cross 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.
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.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2608.04348 [cs.CV]
(or arXiv:2608.04348v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.04348 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1007/978-3-032-31404-8_43 Focus to learn more DOI(s) linking to related resources
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