In this paper, we are interested in pose estimation of animals. Animals
usually exhibit a wide range of variations on poses and there is no available
animal pose dataset for training and testing. To address this problem, we build
an animal pose dataset to facilitate training and evaluation. Considering the
heavy labor needed to label dataset and it is impossible to label data for all
concerned animal species, we, therefore, proposed a novel cross-domain
adaptation method to transform the animal pose knowledge from labeled animal
classes to unlabeled animal classes. We use the modest animal pose dataset to
adapt learned knowledge to multiple animals species. Moreover, humans also
share skeleton similarities with some animals (especially four-footed mammals).
Therefore, the easily available human pose dataset, which is of a much larger
scale than our labeled animal dataset, provides important prior knowledge to
boost up the performance on animal pose estimation. Experiments show that our
proposed method leverages these pieces of prior knowledge well and achieves
convincing results on animal pose estimation.

本文介绍了一种利用跨域适配方法和人类姿态数据集提升多种动物物种姿态估计的方法。实验表明，该方法可以很好地利用先验知识，并取得令人信服的结果。

动物姿势估计的跨域适配

Cross-Domain Adaptation for Animal Pose Estimation

Human in-bed pose estimation has huge practical values in medical and
healthcare applications yet still mainly relies on expensive pressure mapping
(PM) solutions. In this paper, we introduce our novel physics inspired
vision-based approach that addresses the challenging issues associated with the
in-bed pose estimation problem including monitoring a fully covered person in
complete darkness. We reformulated this problem using our proposed Under the
Cover Imaging via Thermal Diffusion (UCITD) method to capture the high
resolution pose information of the body even when it is fully covered by using
a long wavelength IR technique. We proposed a physical hyperparameter concept
through which we achieved high quality groundtruth pose labels in different
modalities. A fully annotated in-bed pose dataset called
Simultaneously-collected multimodal Lying Pose (SLP) is also formed/released
with the same order of magnitude as most existing large-scale human pose
datasets to support complex models' training and evaluation. A network trained
from scratch on it and tested on two diverse settings, one in a living room and
the other in a hospital room showed pose estimation performance of 99.5% and
95.7% in PCK0.2 standard, respectively. Moreover, in a multi-factor comparison
with a state-of-the art in-bed pose monitoring solution based on PM, our
solution showed significant superiority in all practical aspects by being 60
times cheaper, 300 times smaller, while having higher pose recognition
granularity and accuracy.

提出了一种基于 UCITD 方法的姿态识别技术，该技术通过长波长红外线技术对人体进行高分辨率的姿态信息捕捉，形成了一个完全标注的姿态数据集同时也证明其在医疗和医疗保健应用中具有巨大的实用价值。