Domain adaptive pose estimation aims to enable deep models trained on source
domain (synthesized) datasets produce similar results on the target domain
(real-world) datasets. The existing methods have made significant progress by
conducting image-level or feature-level alignment. However, only aligning at a
single level is not sufficient to fully bridge the domain gap and achieve
excellent domain adaptive results. In this paper, we propose a multi-level
domain adaptation aproach, which aligns different domains at the image,
feature, and pose levels. Specifically, we first utilize image style transer to
ensure that images from the source and target domains have a similar
distribution. Subsequently, at the feature level, we employ adversarial
training to make the features from the source and target domains preserve
domain-invariant characeristics as much as possible. Finally, at the pose
level, a self-supervised approach is utilized to enable the model to learn
diverse knowledge, implicitly addressing the domain gap. Experimental results
demonstrate that significant imrovement can be achieved by the proposed
multi-level alignment method in pose estimation, which outperforms previous
state-of-the-art in human pose by up to 2.4% and animal pose estimation by up
to 3.1% for dogs and 1.4% for sheep.

我们提出了一种多层次域适应方法，利用图像风格转换、对抗训练和自监督学习来同时在图像、特征和姿态水平上进行域间对齐，实现了在姿态估计中的显著改进，超越了现有最先进算法，对人体姿态的改进高达 2.4％，对狗的改进高达 3.1％，对羊的改进高达 1.4％。

多级对齐的领域自适应姿态估计

Domain adaptive pose estimation via multi-level alignment

We focus on bridging domain discrepancy in lane detection among different
scenarios to greatly reduce extra annotation and re-training costs for
autonomous driving. Critical factors hinder the performance improvement of
cross-domain lane detection that conventional methods only focus on pixel-wise
loss while ignoring shape and position priors of lanes. To address the issue,
we propose the Multi-level Domain Adaptation (MLDA) framework, a new
perspective to handle cross-domain lane detection at three complementary
semantic levels of pixel, instance and category. Specifically, at pixel level,
we propose to apply cross-class confidence constraints in self-training to
tackle the imbalanced confidence distribution of lane and background. At
instance level, we go beyond pixels to treat segmented lanes as instances and
facilitate discriminative features in target domain with triplet learning,
which effectively rebuilds the semantic context of lanes and contributes to
alleviating the feature confusion. At category level, we propose an adaptive
inter-domain embedding module to utilize the position prior of lanes during
adaptation. In two challenging datasets, ie TuSimple and CULane, our approach
improves lane detection performance by a large margin with gains of 8.8% on
accuracy and 7.4% on F1-score respectively, compared with state-of-the-art
domain adaptation algorithms.

本研究提出了多层领域自适应（MLDA）框架，通过在像素、实例和类别三个补充的语义级别上处理交叉领域车道检测，以识别车道形状和位置先验知识，提出了一种新的视角来解决车道检测领域差异带来的挑战， 针对现有方法只关注像素级损失而忽略车道形状和位置先验知识的不足， 在两个挑战性数据集 TuSimple 和 CULane 中，我们的方法相对于现有领域自适应算法，在准确性和 F1 分数方面分别提高了 8.8％和 7.4％。