Progressive Pseudo Labeling for Multi-Dataset Detection Over Unified Label Space

IF 8.4 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kai Ye;Zepeng Huang;Yilei Xiong;Yu Gao;Jinheng Xie;Linlin Shen
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引用次数: 0

Abstract

Existing multi-dataset detection works mainly focus on the performance of detector on each of the datasets, with different label spaces. However, in real-world applications, a unified label space across multiple datasets is usually required. To address such a gap, we propose a progressive pseudo labeling (PPL) approach to detect objects across different datasets, over a unified label space. Specifically, we employ the widely used architecture of teacher-student model pair to jointly refine pseudo labels and train the unified object detector. The student model learns from both annotated labels and pseudo labels from the teacher model, which is updated by the exponential moving average (EMA) of the student. Three modules, i.e. Entropy-guided Adaptive Threshold (EAT), Global Classification Module (GCM) and Scene-Aware Fusion (SAF) strategy, are proposed to handle the noise of pseudo labels and fit the overall distribution. Extensive experiments are conducted on different multi-dataset benchmarks. The results demonstrate that our proposed method significantly outperforms the State-of-the-Art and is even comparable with supervised methods trained using annotations of all labels.
统一标记空间上多数据集检测的渐进式伪标记
现有的多数据集检测工作主要关注检测器对每个数据集的性能,每个数据集具有不同的标签空间。然而,在实际应用中,通常需要跨多个数据集的统一标签空间。为了解决这种差距,我们提出了一种渐进式伪标记(PPL)方法来检测统一标签空间中不同数据集的对象。具体来说,我们采用广泛使用的师生模型对架构,共同提炼伪标签,训练统一的目标检测器。学生模型从教师模型的标注标签和伪标签中学习,教师模型由学生的指数移动平均线(EMA)更新。提出了熵导自适应阈值(EAT)、全局分类模块(GCM)和场景感知融合(SAF)三个模块来处理伪标签的噪声并拟合整体分布。在不同的多数据集基准上进行了大量的实验。结果表明,我们提出的方法明显优于最先进的方法,甚至可以与使用所有标签注释训练的监督方法相媲美。
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来源期刊
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia 工程技术-电信学
CiteScore
11.70
自引率
11.00%
发文量
576
审稿时长
5.5 months
期刊介绍: The IEEE Transactions on Multimedia delves into diverse aspects of multimedia technology and applications, covering circuits, networking, signal processing, systems, software, and systems integration. The scope aligns with the Fields of Interest of the sponsors, ensuring a comprehensive exploration of research in multimedia.
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