Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation

Vitalii Tutevych; Raphael Memmesheimer; Luca Eichler; Dmytro Pavlichenko; Fynn Schilke; Rodja Krudewig; Sven Behnke

cs.CVApr 24, 2026

Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation

Authors: Vitalii Tutevych, Raphael Memmesheimer, Luca Eichler, Dmytro Pavlichenko, Fynn Schilke, Rodja Krudewig, Sven Behnke

Organizations: Autonomous Intelligent Systems Group, University of Bonn, Germany

Abstract

Reliable object perception is necessary for general-purpose service robots. Open-vocabulary detectors struggle to generalize beyond a few classes and fully supervised training of object detectors requires time-intensive annotations. We present a semi-supervised label propagation approach for household object segmentation. A segment proposer generates class-agnostic masks, and an ensemble of Hopfield networks assigns labels by learning representative embeddings in complementary foundation model embedding spaces (CLIP, ViT, Theia). Our approach scales to 50 object classes with limited annotation overhead and can automatically label 60% of the data in a RoboCup@Home setting, where preparation time is severely constrained. Dataset and code are publicly available at https://github.com/ais-bonn/label_propagation.

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Jun 16, 2026 · cs.CVJ/K move · Enter open · S save

Bounding Box Label Propagation for Re-Annotation of Document Layout Analysis Datasets

Nick Jochum, Tobias Alt-Veit, Christian Schön, Alexander Lück+2

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.

Bounding Box Pseudo-Labeling Multi-Label Classification Pixel-Level Annotation Encoders

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Abstract

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