标题： 时间：用于鲁棒表格图像学习的TabPFN集成多模态引擎 显示英文标题

标题： TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning

摘要： 表格-图像多模态学习，即将结构化表格数据与图像数据相结合，对于各种任务尤其是医学应用具有巨大潜力。 然而，仍存在两个关键挑战：（1）缺乏像视觉和语言领域常见的标准化预训练表格表示；（2）处理表格模态中缺失值的困难，这些缺失值在现实世界的医学数据集中很常见。 为了解决这些问题，我们提出了时间集成多模态引擎（TIME），这是一种基于最近引入的表格基础模型TabPFN的新多模态框架。 TIME利用TabPFN作为冻结的表格编码器生成强大且自然抗缺失数据的嵌入，并将其与来自预训练视觉骨干网络的图像特征结合。 我们探索了一系列融合策略和表格编码器，并在自然和医学数据集上评估了我们的方法。 广泛的实验表明，TIME在完整和不完整的表格输入上始终优于竞争基准，突显了其在实际多模态学习场景中的实用价值。 显示英文摘要

摘要： Tabular-image multimodal learning, which integrates structured tabular data with imaging data, holds great promise for a variety of tasks, especially in medical applications. Yet, two key challenges remain: (1) the lack of a standardized, pretrained representation for tabular data, as is commonly available in vision and language domains; and (2) the difficulty of handling missing values in the tabular modality, which are common in real-world medical datasets. To address these issues, we propose the TabPFN-Integrated Multimodal Engine (TIME), a novel multimodal framework that builds on the recently introduced tabular foundation model, TabPFN. TIME leverages TabPFN as a frozen tabular encoder to generate robust, strong embeddings that are naturally resilient to missing data, and combines them with image features from pretrained vision backbones. We explore a range of fusion strategies and tabular encoders, and evaluate our approach on both natural and medical datasets. Extensive experiments demonstrate that TIME consistently outperforms competitive baselines across both complete and incomplete tabular inputs, underscoring its practical value in real-world multimodal learning scenarios.