Adversarial adaptive methods have been proven to be useful for domain transfer in many fields such as image recognition and semantic segmentation, etc However, for object detection, since each image could have different combinations of objects, brutally aligning all the images without considering their transferability may cause the notorious phenomena named ‘negative transfer’. On the other hand, strong matching the local-level features makes sense, as it not only reduces the discrepancy between different domain distributions, but preserves the category-level semantic information. However, it is hard to markedly achieve domain invariance using a simple adversarial adaptive method. In this work, we propose an effective method termed Local–Global Attentive Adaptation for object Detection (LGAAD). Our method can alleviate the negative transfer caused by improper global alignments through leveraging an adaptively and dynamically weighted transferability to highlight the more transferable images. Furthermore, the proposed method also achieves the strong matching between two domains at local-level features to alleviate the cross-domain discrepancy by using the attention mechanism after multiple local discriminators. Additionally, we also consider the domain impacts of instance-wise features and backgrounds in images with large domain divergence, a non-negligible factor for improving the domain adaptive detection model performance. Extensive experiments of various domain shift scenarios show that our method exceeds the state-of-the-art results on several public datasets. Furthermore, qualitative visualization and ablation analyzes can demonstrate the validity of our approach for attending the interested regions and instances on domain adaptation.

对抗性自适应方法已被证明可用于许多领域的领域转移，例如图像识别和语义分割等。但是，对于对象检测，由于每个图像可能具有不同的对象组合，因此在不考虑其可移植性的情况下残酷地对齐所有图像可能导致臭名昭著的现象，称为“负迁移”。另一方面，强匹配本地级别的功能是有意义的，因为它不仅减少了不同域分布之间的差异，而且保留了类别级别的语义信息。但是，使用简单的对抗性自适应方法很难显着实现域不变性。在这项工作中，我们提出了一种有效的方法，称为用于对象检测的局部全局注意自适应（LGAAD）。我们的方法可以通过利用自适应和动态加权的可转移性来突出显示更具可转移性的图像，从而减轻由于不正确的全局对齐而导致的负向转移。此外，所提出的方法还通过在多个局部鉴别符之后使用注意机制，在局部特征上实现了两个域之间的强匹配，以减轻跨域差异。此外，我们还考虑了具有大域差异的图像中实例实例特征和背景的域影响，这是提高域自适应检测模型性能的不可忽略的因素。各种领域转移方案的大量实验表明，我们的方法超出了几个公共数据集上的最新结果。此外，