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Change detection has received extensive attention because of its realistic significance and broad application fields. However, none of the existing change detection algorithms can handle all scenarios and tasks so far. Different from the most of contributions from the research community in recent years, this paper does not work on designing new change detection algorithms. We, instead, solve the problem from another perspective by enhancing the raw detection results after change detection. As a result, the proposed method is applicable to