  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 (SI), which offers a fresh perspective on interpolation-based
augmentations by relaxing and generalizing Mixup. Based on SI, 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 can consistently
outperform state-of-the-art methods widely adopted for detection. Furthermore,
by jointly leveraging LossMix with unsupervised domain adaptation, we
successfully improve existing approaches and set a new state of the art for
cross-domain object detection.
