Spatial Attention-based Distribution Integration Network for Human Pose Estimation

• URL: http://arxiv.org/abs/2311.05323v1
• Date: Thu, 9 Nov 2023 12:43:01 GMT
• Title: Spatial Attention-based Distribution Integration Network for Human Pose Estimation
• Authors: Sihan Gao, Jing Zhu, Xiaoxuan Zhuang, Zhaoyue Wang, and Qijin Li
• Abstract summary: We present the Spatial Attention-based Distribution Integration Network (SADI-NET) to improve the accuracy of localization. Our network consists of three efficient models: the receptive fortified module (RFM), spatial fusion module (SFM), and distribution learning module (DLM) 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.
• Score: 0.8052382324386398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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