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About this paper
Hanbin Dai, Hailin Shi, Wu Liu, Linfang Wang, Yinglu Liu, Tao Mei
JD AI Research introduces FasterPose, an architecture that redefines efficient human pose estimation by effectively utilizing low-resolution features and a Low-to-High Regressor (LHR) module. It also introduces a Regressive Cross-Entropy (RCE) loss for improved training stability and accuracy, resulting in a 58% FLOPs reduction and 25% smaller model size compared to SimpleBaseline, while also achieving higher accuracy on the COCO dataset.
Key insights
- High-resolution feature representations in human pose estimation lead to substantial computational cost increases with only marginal accuracy improvements.
- Effective utilization of low-resolution features is sufficient for accurate pose estimation and offers significant efficiency gains.
- A custom loss function is crucial for training networks that heavily rely on low-resolution features to ensure faster convergence and better spatial accuracy.
Summary by alphaXiv.
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