Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions

• URL: http://arxiv.org/abs/2407.15451v2
• Date: Tue, 23 Jul 2024 07:22:53 GMT
• Title: Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions
• Authors: Yihao Ai, Yifei Qi, Bo Wang, Yu Cheng, Xinchao Wang, Robby T. Tan,
• Abstract summary: Recent studies on low-light pose estimation require the use of paired well-lit and low-light images with ground truths for training. Our primary novelty lies in leveraging two complementary-teacher networks to generate more reliable pseudo labels. Our method achieves 6.8% (2.4 AP) improvement over the state-of-the-art (SOTA) method.
• Score: 65.0109231252639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing 2D human pose estimation research predominantly concentrates on well-lit scenarios, with limited exploration of poor lighting conditions, which are a prevalent aspect of daily life. Recent studies on low-light pose estimation require the use of paired well-lit and low-light images with ground truths for training, which are impractical due to the inherent challenges associated with annotation on low-light images. To this end, we introduce a novel approach that eliminates the need for low-light ground truths. Our primary novelty lies in leveraging two complementary-teacher networks to generate more reliable pseudo labels, enabling our model achieves competitive performance on extremely low-light images without the need for training with low-light ground truths. Our framework consists of two stages. In the first stage, our model is trained on well-lit data with low-light augmentations. In the second stage, we propose a dual-teacher framework to utilize the unlabeled low-light data, where a center-based main teacher produces the pseudo labels for relatively visible cases, while a keypoints-based complementary teacher focuses on producing the pseudo labels for the missed persons of the main teacher. With the pseudo labels from both teachers, we propose a person-specific low-light augmentation to challenge a student model in training to outperform the teachers. Experimental results on real low-light dataset (ExLPose-OCN) show, our method achieves 6.8% (2.4 AP) improvement over the state-of-the-art (SOTA) method, despite no low-light ground-truth data is used in our approach, in contrast to the SOTA method. Our code will be available at:https://github.com/ayh015-dev/DA-LLPose.

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