Source data-free domain adaptation for a faster R-CNN

作者：

Highlights：

• We proposed a novel prototype-based source data-free domain adaptation method without accessing the source datasets.

• An iteratively updated scheme for global class prototype was proposed to save the category semantic information.

• Combining the semantic information of prototype and image features, a more accurate pseudo-labeling method was proposed.

摘要

•We proposed a novel prototype-based source data-free domain adaptation method without accessing the source datasets.•An iteratively updated scheme for global class prototype was proposed to save the category semantic information.•Combining the semantic information of prototype and image features, a more accurate pseudo-labeling method was proposed.

论文关键词：Source data-free,Object detection,Domain adaptation,Transfer learning

论文评审过程：Received 29 August 2020, Revised 12 September 2021, Accepted 15 November 2021, Available online 19 November 2021, Version of Record 28 February 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108436