基于迁移学习的类别级物体识别与检测研究与进展
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摘要
类别级物体识别与检测属于计算机视觉领域的一个基础性问题,主要研究在图像或视频流中识别和定位出其中感兴趣的物体.在基于小规模数据集的类别级物体识别与检测应用中,模型过拟合、类不平衡和跨领域时特征分布变化等关键问题与挑战交织在一起.本文介绍了迁移学习理论的研究现状,对迁移学习理论解决基于小规模数据集的物体识别与检测中遇到的主要问题的研究思路和前沿技术进行了着重论述和分析.最后对该领域的研究重点和技术发展趋势进行了探讨.
Category-level object recognition and detection are the fundamental problem in computer vision, which aims to solve the challenges of identifying and localizing interested objects in a static image or dynamic video stream. For smallscale data set based category-level object recognition and detection tasks, the key issues and challenges are interwoven,such as model overfitting, class imbalance and cross-domain feature distribution shift. In this survey, we first introduce the research status of transfer learning theory, and then we focus on discussing and analyzing of the research approaches and cutting-edge technologies of how to solve the challenging problems encountered in small-scale data set based object recognition and detection applications. The research emphases and prospective technical development trends are also proposed at the end of this paper.
Category-level object recognition and detection are the fundamental problem in computer vision, which aims to solve the challenges of identifying and localizing interested objects in a static image or dynamic video stream. For smallscale data set based category-level object recognition and detection tasks, the key issues and challenges are interwoven,such as model overfitting, class imbalance and cross-domain feature distribution shift. In this survey, we first introduce the research status of transfer learning theory, and then we focus on discussing and analyzing of the research approaches and cutting-edge technologies of how to solve the challenging problems encountered in small-scale data set based object recognition and detection applications. The research emphases and prospective technical development trends are also proposed at the end of this paper.
引文
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