Summary of Enhancing Table Recognition with Vision Llms: a Benchmark and Neighbor-guided Toolchain Reasoner, by Yitong Zhou et al.
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
by Yitong Zhou, Mingyue Cheng, Qingyang Mao, Qi Liu, Feiyang Xu, Xin Li, Enhong Chen
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 Pre-trained Vision Large Language Models (VLLMs) have made significant progress in understanding structured tables, but their ability to recognize unstructured tables remains under-explored. This work addresses this gap by employing VLLMs in a training-free reasoning paradigm. A benchmark is designed with hierarchical dimensions relevant to table recognition, and pre-trained VLLMs are evaluated for recognizing the structure and content of unstructured tables. The results show that low-quality image input is a significant bottleneck in the recognition process. To address this issue, the Neighbor-Guided Toolchain Reasoner (NGTR) framework is proposed, which integrates multiple lightweight models for low-level visual processing operations. The framework utilizes a neighbor retrieval mechanism to guide the generation of multiple tool invocation plans and introduces a reflection module to supervise the tool invocation process. Extensive experiments on public table recognition datasets demonstrate that the proposed approach significantly enhances the recognition capabilities of vanilla VLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how to better understand unstructured tables using special computer models called Vision Large Language Models (VLLMs). Currently, these models are very good at understanding structured tables, but not as good with unstructured ones. The authors created a benchmark to test the models’ ability to recognize unstructured tables and found that low-quality images make it harder for them to do their job. To solve this problem, they developed a new framework called Neighbor-Guided Toolchain Reasoner (NGTR) that helps VLLMs work better with low-quality images. The results show that their approach improves the models’ ability to recognize unstructured tables. |
