No Data? No Problem: Robust Vision-Tabular Learning with Missing Values 事件

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values arXiv:2512.19602v2 Announce Type: replace Abstract: Large-scale medical biobanks provide imaging data complemented by extensive tabular information, such as clinical measurements or demographics. However, this abundance of tabular attributes does not reflect real-world datasets, where only a subset of attributes may be available. This discrepancy calls for methods that remain robust to missing values at inference. To addres