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English(EN) Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets

新框架使用开放词汇模型进行高效、不确定性感知目标检测标注

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种半监督框架，以减少目标检测数据集所需的手动标注工作量。该方法利用开放词汇模型生成对象表示，然后采用基于图的标签传播算法为未标记的对象分配软标签。该框架量化了偶然不确定性和认知不确定性，在COCO等基准上的评估表明，在该半自动标注数据上训练的检测器，其性能与在完全手动标注数据上训练的检测器相当，而成本却大大降低。 AI

影响 这种方法可以显著降低计算机视觉任务大规模标注数据集的成本和时间。

排序理由 该集群包含一篇研究论文，详细介绍了用于目标检测数据集半监督标注的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架使用开放词汇模型进行高效、不确定性感知目标检测标注

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该集群包含一篇研究论文，详细介绍了用于目标检测数据集半监督标注的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jonathan Klees, Timo Heller, Matthias Rottmann ·

    Open-Vocabulary Models and Label Propagation for Uncertainty-aware Pseudo-Labeling of Object Detection Datasets

    arXiv:2610.29788v1 Announce Type: new Abstract: Object detection models rely on large-scale annotated datasets that require substantial manual annotation effort. The labeling costs further increase when annotation uncertainty is captured through soft labels, as multiple annotatio…

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