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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



... 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.



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