Cross-domain visual problems, such as image-to-image translation and domain adaptive object detection, have attracted increasing attentions in the last few years, and also become new rising and challenging directions for the computer vision community. Recently, despite enormous efforts of the field in data collection, there are still few datasets covering the instance-level image-to-image translation and domain adaptive object detection tasks simultaneously. In this work, we introduce a large-scale cross-domain benchmark CDTD (contains 155,529 high-resolution natural images across four different modalities with object bounding box annotations. A summary of the entire dataset is provided in the following sections. Dataset is available at: http://zhiqiangshen.com/projects/INIT/index.html .) for the new instance-level translation and object detection tasks. We provide comprehensive baseline results of the benchmark on both of these two tasks. Moreover, we proposed a novel instance-level image-to-image translation approach called INIT and a gradient detach method for the domain adaptive object detection to harvest and exert dataset’s function of the instance level annotations across different domains.

中文翻译：

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CDTD：用于实例级图像到图像转换和域自适应对象检测的大规模跨域基准测试

跨域视觉问题，例如图像到图像的转换和域自适应对象检测，在过去几年中越来越受到关注，也成为计算机视觉社区新的兴起和具有挑战性的方向。最近，尽管该领域在数据收集方面做出了巨大努力，但同时涵盖实例级图像到图像转换和域自适应对象检测任务的数据集仍然很少。在这项工作中，我们引入了一个大规模跨域基准 CDTD（包含跨越四种不同模式的 155,529 张高分辨率自然图像，带有对象边界框注释。整个数据集的摘要在以下部分中提供。数据集可在：http://zhiqiangshen.com/projects/INIT/index.html。) 用于新的实例级翻译和对象检测任务。我们提供了这两项任务的基准测试的综合基线结果。此外，我们提出了一种称为 INIT 的新型实例级图像到图像转换方法，以及一种用于域自适应对象检测的梯度分离方法，以跨不同域收集和发挥数据集的实例级注释功能。