Pyramid Knowledge Distillation for Efficient Human Pose Estimation

Human pose estimation is an important task in many real-time applications. Existing methods directly slim the CNN by deploying well-designed lightweight modules. However, these methods lack privileged information guidance and the knowledge distillation technique stays less explored. In this work, we...

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Bibliographic Details
Published in2022 IEEE International Conference on Image Processing (ICIP) pp. 2177 - 2181
Main Authors Li, Yang, Jiao, Peng, Wang, Haoqian
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.10.2022
Subjects
Benchmark testing
Computational modeling
Correlation
Costs
Deep learning
Human pose estimation
Image coding
Knowledge distillation
Pose estimation
Real-time systems
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Summary:Human pose estimation is an important task in many real-time applications. Existing methods directly slim the CNN by deploying well-designed lightweight modules. However, these methods lack privileged information guidance and the knowledge distillation technique stays less explored. In this work, we propose a novel method, namely Pyramid Knowledge Distillation (PKD) for efficient human pose estimation. Specifically, PKD composes of Pyramid Structured Map Distillation (PSMD) and Pyramid Feature Map Distillation (PFMD). In PSMD, we formulate a structured map encoding robust interjoint correlation. Based on structured map, the spatial dependencies between keypoints can be better transferred from a cumbersome teacher network to a compact student model. To further promote the efficiency of student, PFMD is used to distill rich local and global features from teacher. Experiments demonstrate that PKD achieves an optimal trade-off between cost and accuracy on COCO and MPII benchmarks, even with a much faster inference speed.
ISSN:2381-8549
DOI:10.1109/ICIP46576.2022.9897536