The inherent characteristics and light fluctuations of water bodies give rise
to the huge difference between different layers and regions in underwater
environments. When the test set is collected in a different marine area from
the training set, the issue of domain shift emerges, significantly compromising
the model's ability to generalize. The Domain Adversarial Learning (DAL)
training strategy has been previously utilized to tackle such challenges.
However, DAL heavily depends on manually one-hot domain labels, which implies
no difference among the samples in the same domain. Such an assumption results
in the instability of DAL. This paper introduces the concept of Domain
Similarity-Perceived Label Assignment (DSP). The domain label for each image is
regarded as its similarity to the specified domains. Through domain-specific
data augmentation techniques, we achieved state-of-the-art results on the
underwater cross-domain object detection benchmark S-UODAC2020. Furthermore, we
validated the effectiveness of our method in the Cityscapes dataset.

该研究通过引入 “领域相似 - 感知标签分配”（Domain Similarity-Perceived Label Assignment）的概念，并结合领域特定的数据增强技术，在水下跨域目标检测基准测试 S-UODAC2020 上取得了最先进的结果，并验证了该方法在 Cityscapes 数据集上的有效性。