论文标题
深区自适应对象检测：调查
Deep Domain Adaptive Object Detection: a Survey
论文作者
论文摘要
基于深度学习（DL）的对象检测取得了巨大进展。这些方法通常假设可以使用大量标记的培训数据，并且培训和测试数据是从相同的分布中汲取的。但是，这两个假设在实践中并不总是存在。深层域自适应对象检测（DDAOD）已成为一种新的学习范式，以应对上述挑战。本文旨在回顾深域自适应对象检测方法的最新进展。首先，我们简要介绍了深层域适应的基本概念。其次，将深域自适应检测器分为五个类别，并提供了每个类别中代表性方法的详细描述。最后，提供了对未来研究趋势的见解。
Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented.
