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For this paper, we have access to images with instance-level\nannotations in a source domain (e.g., natural image) and images with\nimage-level annotations in a target domain (e.g., watercolor). In addition, the\nclasses to be detected in the target domain are all or a subset of those in the\nsource domain. Starting from a fully supervised object detector, which is\npre-trained on the source domain, we propose a two-step progressive domain\nadaptation technique by fine-tuning the detector on two types of artificially\nand automatically generated samples. 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