Ying Tian1, *, Libing Wang1, Hexin Gu2, Lin Fan3
CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2397-2412, 2020, DOI:10.32604/cmc.2020.011386
Abstract The application of deep learning in the field of object detection has
experienced much progress. However, due to the domain shift problem, applying an
off-the-shelf detector to another domain leads to a significant performance drop. A
large number of ground truth labels are required when using another domain to train
models, demanding a large amount of human and financial resources. In order to avoid
excessive resource requirements and performance drop caused by domain shift, this
paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our
approach improves the cross-domain vehicle detection model from image space and
feature space.… More >
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