Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation

• URL: http://arxiv.org/abs/2007.08354v2
• Date: Fri, 20 Aug 2021 10:53:38 GMT
• Title: Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation
• Authors: Vinkle Srivastav, Afshin Gangi, Nicolas Padoy
• Abstract summary: 2D/3D human pose estimation is needed to develop novel intelligent tools for the operating room. We propose to use knowledge distillation in a teacher/student framework to harness the knowledge present in a large-scale non-annotated dataset. The easily deployable network trained using this effective self-supervision strategy performs on par with the teacher network on emphMVOR+, an extension of the public MVOR dataset.
• Score: 2.8802646903517957
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 2D/3D human pose estimation is needed to develop novel intelligent tools for the operating room that can analyze and support the clinical activities. The lack of annotated data and the complexity of state-of-the-art pose estimation approaches limit, however, the deployment of such techniques inside the OR. In this work, we propose to use knowledge distillation in a teacher/student framework to harness the knowledge present in a large-scale non-annotated dataset and in an accurate but complex multi-stage teacher network to train a lightweight network for joint 2D/3D pose estimation. The teacher network also exploits the unlabeled data to generate both hard and soft labels useful in improving the student predictions. The easily deployable network trained using this effective self-supervision strategy performs on par with the teacher network on \emph{MVOR+}, an extension of the public MVOR dataset where all persons have been fully annotated, thus providing a viable solution for real-time 2D/3D human pose estimation in the OR.

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