Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns. Instead, this paper considers a realistic problem setting called source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain. For the challenging problem, we first construct a teacher-student learning baseline by stabilizing the predictions under data augmentation and network ensembles. Built on this, we further propose a unified approach, Mixup Augmentation and Progressive Selection (MAPS), to fully exploit the noisy pseudo labels of unlabeled target data during training. On the one hand, MAPS regularizes the model to favor simple linear behavior in-between the target samples via self-mixup augmentation, preventing the model from over-fitting to noisy predictions. On the other hand, MAPS employs the self-paced learning paradigm and progressively selects pseudo-labeled samples from `easy' to `hard' into the training process to reduce noise accumulation. Results on four keypoint detection datasets show that MAPS outperforms the baseline and achieves comparable or even better results in comparison to previous non-source-free counterparts.

翻译：现有跨域关键点检测方法在适配过程中通常需要访问源域数据，这可能违反数据隐私法规并引发严重的安全问题。为此，本文考虑一种名为无源域自适应关键点检测的现实问题设定，即仅向目标域提供训练好的源模型。针对这一挑战性问题，我们首先通过数据增强和网络集成稳定预测结果，构建了一个师生学习基线。在此基础上，我们进一步提出统一方法——混合增强与渐进选择（MAPS），以在训练过程中充分利用未标注目标数据的含噪伪标签。一方面，MAPS通过自混合增强正则化模型，使其倾向于目标样本间的简单线性行为，从而防止模型过拟合噪声预测。另一方面，MAPS采用自步学习范式，将伪标签样本从"简单"到"困难"逐步纳入训练过程，以减少噪声累积。在四个关键点检测数据集上的实验结果表明，MAPS优于基线方法，并且与以往非无源域方法相比达到相当甚至更优的性能。