Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910164030849024 |
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| author | Tutevych, Vitalii Memmesheimer, Raphael Eichler, Luca Pavlichenko, Dmytro Schilke, Fynn Krudewig, Rodja Behnke, Sven |
| author_facet | Tutevych, Vitalii Memmesheimer, Raphael Eichler, Luca Pavlichenko, Dmytro Schilke, Fynn Krudewig, Rodja Behnke, Sven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22992 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation Tutevych, Vitalii Memmesheimer, Raphael Eichler, Luca Pavlichenko, Dmytro Schilke, Fynn Krudewig, Rodja Behnke, Sven Computer Vision and Pattern Recognition Robotics 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. |
| title | Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2604.22992 |