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• W3118508671 abstract "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." @default.
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• W3118508671 date "2020-12-01" @default.
• W3118508671 modified "2023-09-30" @default.
• W3118508671 title "Deep Domain Adaptive Object Detection: a Survey" @default.
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• W3118508671 doi "https://doi.org/10.1109/ssci47803.2020.9308604" @default.
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