Summary of Multimodal Table Understanding, by Mingyu Zheng et al.
Multimodal Table Understanding
by Mingyu Zheng, Xinwei Feng, Qingyi Si, Qiaoqiao She, Zheng Lin, Wenbin Jiang, Weiping Wang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new challenge in table understanding, focusing on multimodal table understanding that directly uses table images as input. Previous methods rely on converting tables into text sequences, which can be difficult to access in real-world scenarios. The authors construct a large-scale dataset called MMTab, covering various table images, instructions, and tasks. They then develop Table-LLaVA, a tabular multimodal large language model (MLLM) that outperforms recent baselines on 23 benchmarks under both held-in and held-out settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers understand tables better. Right now, most methods require tables to be turned into text first, which can be tricky in real-life situations. But what if we could make computers understand tables just by looking at the pictures of them? The authors created a big dataset with many table images and instructions, and then made a special computer program that can understand these tables really well. This new way of understanding tables is super useful and can help us do lots of cool things! |
Keywords
» Artificial intelligence » Large language model
