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Indexed by:期刊论文

Date of Publication:2022-06-29

Journal:自动化学报

Volume:45

Issue:7

Page Number:1224-1243

ISSN No.:0254-4156

Abstract: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 small-scale 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. Copyright ? 2019 Acta Automatica Sinica. All rights reserved.

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