Spatial Attention-based Distribution Integration Network for Human Pose
  Estimation
Sihan Gao Jing Zhu Xiaoxuan Zhuang Zhaoyue Wang Qijin Li
In recent years, human pose estimation has made significant progress through
the implementation of deep learning techniques. However, these techniques still
face limitations when confronted with challenging scenarios, including
occlusion, diverse appearances, variations in illumination, and overlap. To
cope with such drawbacks, we present the Spatial Attention-based Distribution
Integration Network (SADI-NET) to improve the accuracy of localization in such
situations. Our network consists of three efficient models: the receptive
fortified module (RFM), spatial fusion module (SFM), and distribution learning
module (DLM). Building upon the classic HourglassNet architecture, we replace
the basic block with our 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. For the purpose of determining the efficacy of our model,
we conducted extensive experiments on the MPII and LSP benchmarks.
Particularly, our model obtained a remarkable $92.10\%$ percent accuracy on the
MPII test dataset, demonstrating significant improvements over existing models
and establishing state-of-the-art performance.