VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning
Multi-model learning has attracted great attention in visual-text tasks. However, visual-tabular data, which plays a pivotal role in high-stakes domains like healthcare and industry, remains underexplored. In this paper, we introduce \textit{VT-Bench}, the first unified benchmark for standardizing vision-tabular discriminative prediction and generative reasoning tasks. VT-Bench aggregates 14 datasets across 9 domains (medical-centric, while covering pets, media, and transportation) with over 756K samples. We evaluate 23 representative models, including unimodal experts, specialized visual-tabular models, general-purpose vision-language models (VLMs), and tool-augmented methods, highlighti...
| Authors | Zi-Yi Jia, Zi-Jian Cheng, Xin-Yue Zhang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 26 May 2026 |
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