摘要
基于主动学习的目标检测通常使用更少的标注数据来提高目标检测模型的性能,即学习者可以从大量未标注样本中选取有价值的样本进行人工标注,并通过迭代训练来优化模型.然而,现有基于主动学习的目标检测方法仍然无法有效平衡样本不确定性和多样性,且查询样本冗余度高.为了解决这一问题,提出一种不确定度引导的对抗主动学习目标检测方法.首先,引入损失预测模块评估未标注样本的不确定度,并利用不确定度引导对抗网络训练,构建具有不确定性和多样性的查询样本集;其次,基于特征相似度评估样本差异性,降低查询样本的冗余度;最后,采用多种目标检测框架在MS COCO数据集与Pascal VOC数据集上进行实验,所提方法能在较少标注下有效提升目标检测精度.
Abstract
Object detection based on active learning typically utilizes limited labeled data to enhance detection model performance.This method allows learners to select valuable samples from a large pool of unlabeled data for manual labeling and to iteratively train and optimize the model.However,existing object detection methods that use active learning often struggle to effectively balance sample uncertainty and diversity,which results in high redundancy of query samples.To address this issue,we propose an adversarial active learning method guided by uncertainty for object detection.First,we introduce a loss prediction module to evaluate the uncertainty of unlabeled samples.This uncertainty guides the adversarial network training and helps construct a query sample set that includes both uncertainty and diversity.Second,we evaluate sample diversity based on feature similarity to reduce redundancy of query samples.Finally,experimental results on the MS COCO and Pascal VOC datasets using multiple detection frameworks demonstrate that the proposed method can effectively improve object detection accuracy with fewer annotations.
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