用主动学习减少表格检测标注量，效果优于随机选样。
Table Detection with Active Learning
- 结合不确定性与多样性选择样本，提升标注效率。
- 相同标注预算下，mAP比随机采样更高，接近全监督模型。
- 适合数据标注成本高的表格检测场景，尤其对小样本有效。
高效的数据标注仍是机器学习中的关键挑战，尤其在需要大量标注数据的目标检测任务中。主动学习（AL）通过选择最具信息量的样本，成为降低标注成本的有前景方案。传统AL方法多依赖不确定性选样，但近期研究表明，引入多样性策略可提升目标检测中的采样效率。本文方法确保选取具有代表性的样本，从而增强模型泛化能力。我们在两个基准数据集（TableBank-LaTeX、TableBank-Word）上，采用CascadeTabNet和YOLOv9等先进表格检测架构进行评估。结果表明，基于AL的样本选择显著优于随机采样，在有限标注预算下仍能保持与全监督模型相当的性能，且在相同标注预算下实现更高的mAP得分。
原文摘要 · Abstract (English)
Efficient data annotation remains a critical challenge in machine learning, particularly for object detection tasks requiring extensive labeled data. Active learning (AL) has emerged as a promising solution to minimize annotation costs by selecting the most informative samples. While traditional AL approaches primarily rely on uncertainty-based selection, recent advances suggest that incorporating diversity-based strategies can enhance sampling efficiency in object detection tasks. Our approach ensures the selection of representative examples that improve model generalization. We evaluate our method on two benchmark datasets (TableBank-LaTeX, TableBank-Word) using state-of-the-art table detection architectures, CascadeTabNet and YOLOv9. Our results demonstrate that AL-based example selection significantly outperforms random sampling, reducing annotation effort given a limited budget while maintaining comparable performance to fully supervised models. Our method achieves higher mAP scores within the same annotation budget.
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