Automated analysis of mouse behaviours is crucial for many applications in neuroscience. However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours. Although deep learning based methods have made promising advances in human pose estimation, they cannot be directly applied to pose estimation of mice due to different physiological natures. Particularly, since mouse body is highly deformable, it is a challenge to accurately locate different keypoints on the mouse body. In this paper, we propose a novel Hourglass network based model, namely Graphical Model based Structured Context Enhancement Network (GM-SCENet) where two effective modules, i.e., Structured Context Mixer (SCM) and Cascaded Multi-Level Supervision (CMLS) are subsequently implemented. SCM can adaptively learn and enhance the proposed structured context information of each mouse part by a novel graphical model that takes into account the motion difference between body parts. Then, the CMLS module is designed to jointly train the proposed SCM and the Hourglass network by generating multi-level information, increasing the robustness of the whole network.Using the multi-level prediction information from SCM and CMLS, we develop an inference method to ensure the accuracy of the localisation results. Finally, we evaluate our proposed approach against several baselines...

翻译：对鼠标行为进行自动化分析对于神经科学的许多应用至关重要。然而,从视频或图像中量化鼠标行为仍是一个具有挑战性的问题,在描述鼠标行为方面,估计可能具有重要作用。虽然深层次学习方法在人造图示方面取得了有希望的进展,但由于生理性质不同,这些方法不能直接用于估计小鼠。特别是,由于鼠体高度变形,精确定位鼠体上不同的关键点是一项挑战。在本文件中,我们提议建立一个基于新颖的沙漏网络模型,即基于图形模型的基于结构化环境增强网络(GM-SCENet),在这个模型中,有两个有效的模块,即结构化背景混合和累加多级监督(CMLS),在描述鼠体外行为方面都取得了可喜的进展。由于鼠标体体的体格高度变,因此很难准确定位鼠标体上不同的关键点。然后,CMLS模块旨在联合培训拟议的SCM和Hyleclas网络,通过生成多层次的信息,提高整个网络的稳性,随后实施。UCMLS最后评估我们提出的多层次基线方法。