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.
In this article, the authors discuss the challenges faced in underwater cross-domain object detection and introduce a novel approach called Domain Similarity-Perceived Label Assignment (DSP). They highlight the issue of domain shift when the test set is collected in a different marine area from the training set, leading to poor model generalization. Previous approaches like Domain Adversarial Learning (DAL) suffer from instability due to their dependency on manually assigned domain labels.
The authors propose DSP as a solution, where the domain label for each image is determined based on its similarity to specified domains. This approach allows for a more flexible and nuanced understanding of the images’ characteristics within a domain. By employing domain-specific data augmentation techniques, the authors were able to achieve state-of-the-art results on the S-UODAC2020 benchmark for underwater cross-domain object detection.
What stands out about this research is its multi-disciplinary nature. It combines computer vision techniques with domain adaptation strategies to address the unique challenges of underwater environments. By taking into account the inherent characteristics and light fluctuations of water bodies, the authors devise a method that effectively handles domain shift.
Furthermore, the authors demonstrate the effectiveness of their proposed DSP approach by validating it on the Cityscapes dataset, which showcases the versatility of their method beyond just underwater settings. This suggests that their approach may have broader applications in various domains.
In conclusion, this paper presents an innovative solution to address domain shift in underwater cross-domain object detection. The introduction of Domain Similarity-Perceived Label Assignment (DSP) provides a more nuanced understanding of image characteristics within domains and improves model generalization. By achieving state-of-the-art results on underwater benchmarks and demonstrating effectiveness on a different dataset, this research opens up new possibilities for improved object detection across different domains.