Table Detection with Active Learning

Gautam, Somraj

doi:10.1007/978-3-032-04624-6_8

2026-01-01

PRE

Gautam, Somraj *

Nachiketa Purohit

Gaurav Harit

DOI：10.1007/978-3-032-04624-6_8

原文链接

摘要

En 中文

高效数据标注仍然是机器学习领域的重大挑战，尤其是在需要大量标注数据的物体检测任务中。主动学习（AL）作为一种有前景的解决方案，通过选择最具信息量的样本，旨在最小化标注成本。尽管传统的AL方法主要依赖基于不确定性的选择，但近期进展表明，将基于多样性的策略融入其中可以提升物体检测任务的采样效率。我们的方法确保选择具有代表性的样例以改善模型泛化能力。我们在两个基准数据集（TableBank-LaTeX、TableBank-Word）上评估了该方法，采用最新的表格检测架构CascadeTabNet和YOLOv9。结果表明，基于AL的样例选择显著优于随机采样，在有限的预算下减少了标注工作量，同时保持与全监督模型相当的性能。我们的方法在相同的标注预算下实现了更高的mAP分数。

Keyword:

Active learning

Table detection

Diversity-based

Uncertainty-based

期刊

DOCUMENT ANALYSIS AND RECOGNITION-ICDAR 2025, PT III

IF:

论文数:

被引数:

机构

indian institute of technology system (iit system)

学者数:

9.5W

论文数: 9.9W

被引数: 4.3W

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