Get human pose using attention mechanism to expand receptive fields with SADI-NET
Get human pose using attention mechanism to expand receptive fields with SADI-NET
Spatial Attention-based Distribution Integration Network for Human Pose Estimation
arXiv paper abstract https://arxiv.org/abs/2311.05323
arXiv PDF paper https://arxiv.org/pdf/2311.05323.pdf
... human pose estimation ... face limitations ... with challenging scenarios, including occlusion, diverse appearances, variations in illumination, and overlap ... present the Spatial Attention-based Distribution Integration Network (SADI-NET) to improve the accuracy
... network consists of three efficient models: the receptive fortified module (RFM), spatial fusion module (SFM), and distribution learning module (DLM).
Building upon ... HourglassNet architecture, ... replace the basic block with ... proposed RFM. The RFM incorporates a dilated residual block and attention mechanism to expand receptive fields while enhancing sensitivity to spatial information.
In addition, the SFM incorporates multi-scale characteristics by employing both global and local attention mechanisms.
Furthermore, the DLM, inspired by residual log-likelihood estimation (RLE), reconfigures a predicted heatmap using a trainable distribution weight.
... model ... demonstrating significant improvements over existing models and establishing state-of-the-art performance.
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
Comments