The success of data mixing augmentations in image classification tasks has
been well-received. However, these techniques cannot be readily applied to
object detection due to challenges such as spatial misalignment,
foreground/background distinction, and plurality of instances. To tackle these
issues, we first introduce a novel conceptual framework called Supervision
Interpolation, which offers a fresh perspective on interpolation-based
augmentations by relaxing and generalizing Mixup. Building on this framework,
we propose LossMix, a simple yet versatile and effective regularization that
enhances the performance and robustness of object detectors and more. Our key
insight is that we can effectively regularize the training on mixed data by
interpolating their loss errors instead of ground truth labels. Empirical
results on the PASCAL VOC and MS COCO datasets demonstrate that LossMix
consistently outperforms currently popular mixing strategies. Furthermore, we
design a two-stage domain mixing method that leverages LossMix to surpass
Adaptive Teacher (CVPR 2022) and set a new state of the art for unsupervised
domain adaptation.

该研究提出了一种新的概念框架 —— 监督插值，通过松弛和概括 Mixup，可以有效地规范混合数据的训练，进而提出了一种名为 LossMix 的简单却灵活且有效的正则化方法，并设计了一种两阶段的域混合方法，借助 LossMix 超越了 Adaptive Teacher，并在无监督领域适应中有了新的突破。