论文标题
评估零成本主动学习以进行对象检测
Evaluating Zero-cost Active Learning for Object Detection
论文作者
论文摘要
对象检测需要大量的标签工作来学习强大的模型。积极的学习可以通过智能选择要注释的相关示例来减少这项工作。但是，正确选择这些示例而不引入对概括性能产生负面影响的采样偏差并不是直接的，并且大多数活跃的学习技术无法在现实世界基准上坚持他们的承诺。在我们的评估论文中，我们专注于没有计算开销的主动学习技术，除了推理外，我们称之为零成本的主动学习。特别是，我们表明，关键成分不仅是边界框级别上的分数，而且是用于汇总分数排名图像的技术。我们概述了我们的实验设置，并在使用主动学习进行对象检测时也讨论了实际的注意事项。
Object detection requires substantial labeling effort for learning robust models. Active learning can reduce this effort by intelligently selecting relevant examples to be annotated. However, selecting these examples properly without introducing a sampling bias with a negative impact on the generalization performance is not straightforward and most active learning techniques can not hold their promises on real-world benchmarks. In our evaluation paper, we focus on active learning techniques without a computational overhead besides inference, something we refer to as zero-cost active learning. In particular, we show that a key ingredient is not only the score on a bounding box level but also the technique used for aggregating the scores for ranking images. We outline our experimental setup and also discuss practical considerations when using active learning for object detection.
