Available 3D human pose estimation approaches leverage different forms of
strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring
synthetic or in-studio domains, acquiring such supervision for each new target
environment is highly inconvenient. To this end, we cast 3D pose learning as a
self-supervised adaptation problem that aims to transfer the task knowledge
from a labeled source domain to a completely unpaired target. We propose to
infer image-to-pose via two explicit mappings viz. image-to-latent and
latent-to-pose where the latter is a pre-learned decoder obtained from a
prior-enforcing generative adversarial auto-encoder. Next, we introduce
relation distillation as a means to align the unpaired cross-modal samples i.e.
the unpaired target videos and unpaired 3D pose sequences. To this end, we
propose a new set of non-local relations in order to characterize long-range
latent pose interactions unlike general contrastive relations where positive
couplings are limited to a local neighborhood structure. Further, we provide an
objective way to quantify non-localness in order to select the most effective
relation set. We evaluate different self-adaptation settings and demonstrate
state-of-the-art 3D human pose estimation performance on standard benchmarks.

本研究提出了一种自我监督的适应方法，将 3D 姿势学习转换为源域标记到未标记目标域的知识转移，通过两个显式映射推断图像到姿势，提出关系蒸馏作为一种方法来对齐未配对的跨模态样本，并使用新的非局部关系来表征长程潜在姿态相互作用，在标准基准测试中展示了最先进的 3D 人体姿态估计性能。

非本地隐变量关系蒸馏用于自适应三维人体姿态估计

Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation

Recent work has shown the importance of adaptation of broad-coverage
contextualised embedding models on the domain of the target task of interest.
Current self-supervised adaptation methods are simplistic, as the training
signal comes from a small percentage of \emph{randomly} masked-out tokens. In
this paper, we show that careful masking strategies can bridge the knowledge
gap of masked language models (MLMs) about the domains more effectively by
allocating self-supervision where it is needed. Furthermore, we propose an
effective training strategy by adversarially masking out those tokens which are
harder to reconstruct by the underlying MLM. The adversarial objective leads to
a challenging combinatorial optimisation problem over \emph{subsets} of tokens,
which we tackle efficiently through relaxation to a variational lowerbound and
dynamic programming. On six unsupervised domain adaptation tasks involving
named entity recognition, our method strongly outperforms the random masking
strategy and achieves up to +1.64 F1 score improvements.

本文探讨了如何利用适当的遮罩策略来提高基于掩蔽的语言模型在领域自适应任务上的性能，并提出了一种有效的训练策略，即通过对更难以恢复的标记进行对抗性遮蔽，以使模型更好地适应目标任务。通过六个无监督领域适应任务，该方法在命名实体识别方面显著优于随机遮蔽策略，提高了 1.64 个 F1 分数。