To Perceive or Not to Perceive: Lightweight Stacked Hourglass Network
This work addresses efficiency for real-time pose estimation applications, but it is incremental as it builds on existing stacked hourglass architectures.
The authors tackled the problem of reducing computational cost in human pose estimation by designing a lightweight version of the stacked hourglass network, achieving a 79% reduction in parameters with only a marginal drop in performance.
Human pose estimation (HPE) is a classical task in computer vision that focuses on representing the orientation of a person by identifying the positions of their joints. We design a lighterversion of the stacked hourglass network with minimal loss in performance of the model. The lightweight 2-stacked hourglass has a reduced number of channels with depthwise separable convolutions, residual connections with concatenation, and residual connections between the necks of the hourglasses. The final model has a marginal drop in performance with 79% reduction in the number of parameters and a similar drop in MAdds