用半监督方法自动标注家居物体，减少人工标注量。
Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation
- 通过生成无类别的掩码，再用霍普菲尔德网络在多个模型空间中学习代表性嵌入来分配标签。
- 在RoboCup@Home数据集上自动标注60%的数据，仅需少量人工标注即可支持50个物体类别。
- 适合需要快速部署的机器人场景，尤其适合标注资源受限的应用。
可靠的目标感知对通用服务机器人至关重要。开放词汇检测器难以泛化到少数类别之外，而完全依赖人工标注训练目标检测器则耗时费力。本文提出一种针对家庭物体分割的半监督标签传播方法：先由一个无关类别的掩码生成器产生候选掩码，再利用一组霍普菲尔德网络在互补的基础模型嵌入空间（CLIP、ViT、Theia）中学习代表性嵌入以分配标签。该方法在仅需少量标注的情况下可扩展至50个物体类别，并在RoboCup@Home设置中实现60%数据的自动标注，极大缓解了准备时间紧张的问题。相关数据集与代码已公开于https://github.com/ais-bonn/label_propagation。
原文摘要 · Abstract (English)
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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