Datasets in practical document processing scenarios typically grow over time, and their class annotations undergo continuous refinement. This creates significant re-annotation efforts, which are time-consuming and costly. A promising remedy is to re-annotate only a small subset of available documents manually and apply semi-supervised learning techniques that leverage both labelled and unlabelled data. Although there are numerous approaches to tackle this problem for classification, there exists no adaptation for the problem of re-classifying object detection instances, e.g. for document layout analysis. To this end, we propose Bounding Box Label Propagation (BBLP), a pseudo-labelling framework for object detection. An object encoder integrates visual, textual, and positional embeddings from object detection samples to come up with a joint embedding that can be used for Label Propagation on partially annotated datasets in a plug-and-play fashion. Evaluation results indicate that the proposed approach produces high-quality class annotations of bounding boxes. In the D4LA layout analysis dataset, it achieves a mAP of 54.0%, corresponding to 81.6% of fully supervised performance, while using only 10% labelled data. Our work demonstrates the potential of Label Propagation for object detection and lays the groundwork for reducing manual annotation efforts in real-world document processing applications.

翻译：在实际文档处理场景中，数据集通常随时间增长，其类别标注也持续细化更新。这产生了大量耗时耗资的重标注工作。一种有前景的解决方案是仅对少量可用文档进行人工重标注，并应用同时利用标注与未标注数据的半监督学习技术。尽管针对分类问题已有多种处理该问题的方案，但目前尚无适用于目标检测实例重分类（例如文档布局分析）的改编方法。为此，我们提出边界框标签传播（BBLP），一种面向目标检测的伪标签框架。该框架通过目标编码器整合目标检测样本的视觉、文本及位置嵌入，生成可即插即用地对部分标注数据集进行标签传播的联合嵌入。评估结果表明，所提方法能够为边界框生成高质量的类别标注。在D4LA布局分析数据集中，该方法仅使用10%的标注数据即可达到54.0%的mAP，对应全监督性能的81.6%。我们的工作展示了标签传播在目标检测领域的潜力，并为减少真实文档处理应用中的人工标注工作量奠定了基础。