Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training

• URL: http://arxiv.org/abs/2303.15147v1
• Date: Mon, 27 Mar 2023 12:32:49 GMT
• Title: Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training
• Authors: Mohammad Rezaei, Farnaz Farahanipad, Alex Dillhoff, Vassilis Athitsos
• Abstract summary: We propose a semi-supervised method to significantly reduce the dependence on labeled training data. The proposed method consists of two identical networks trained jointly: a teacher network and a student network. Experiments demonstrate that the proposed method outperforms the state-of-the-art semi-supervised methods by large margins.
• Score: 2.6954666679827137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the significant progress that depth-based 3D hand pose estimation methods have made in recent years, they still require a large amount of labeled training data to achieve high accuracy. However, collecting such data is both costly and time-consuming. To tackle this issue, we propose a semi-supervised method to significantly reduce the dependence on labeled training data. The proposed method consists of two identical networks trained jointly: a teacher network and a student network. The teacher network is trained using both the available labeled and unlabeled samples. It leverages the unlabeled samples via a loss formulation that encourages estimation equivariance under a set of affine transformations. The student network is trained using the unlabeled samples with their pseudo-labels provided by the teacher network. For inference at test time, only the student network is used. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art semi-supervised methods by large margins.

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