WildTableBench: Benchmarking Multimodal Foundation Models on Table Understanding In the Wild
Using multimodal foundation models to analyze table images is a high-value yet challenging application in consumer and enterprise scenarios. Despite its importance, current evaluations rely largely on structured-text tables or clean rendered images, leaving the visual complexity of in-the-wild table images underexplored. Such images feature varied layouts and diverse domains that demand sophisticated structural perception and numerical reasoning. To bridge this gap, we introduce WildTableBench, the first question-answering benchmark for naturally occurring table images from real-world settings. WildTableBench comprises 402 high-information-density table images collected from online forums...
| Authors | Junzhe Huang, Xiaoxiao Sun, Yan Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 May 2026 |
| Last updated at source | 21 May 2026 |
Models and datasets referencing this paper
- jzhuang/WildTableBenchDataset
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