Aggregated pyramid gating network for human pose estimation without pre-training. (June 2023)

• Record Type: Journal Article
• Authors: Jiang, Chenru; Huang, Kaizhu; Zhang, Shufei; Wang, Xinheng; Xiao, Jimin; Goulermas, Yannis
• Is Part Of: Pattern recognition. Volume 138(2023)
• Publication Date: 2023-06
• Subjects: Pyramid gating system -- Stabilization -- Human pose estimation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109429
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We present a multi-stage residual feature pyramid gating strategy which designs soft gates to select and reinforce multi-scale semantic features. We introduce an image pyramid gating strategy to preserve natural information to enhance network discrimination in an attention-based fashion. Our method attains the best performance without pre-training and can be readily combined with general frameworks without structure modifications. Specifically, the prediction accuracy consistently achieves over 3% improvements than the baseline (without pre-training) under various model configurations. Abstract: In this work, we propose a comprehensive aggregated residual gating structure, the Pyramid GAting Network (PGA-Net) for human pose estimation which can select, distill, and fuse semantic level and natural level information from multiple scales. In comparison, through utilizing multi-scale features, most existing state-of-the-art pose estimation methods are still limited in three aspects. First, multi-scale features contain massively redundant information, which is unfortunately not distilled by most existing approaches. Second, preferring deeper network structures to extract strong semantic features, the conventional methods often ignore original texture information fusion. Third, to attain a good parameter initialization, the current methods heavily rely on pre-training, which is very time-consuming or even unavailable. While better coping with the above problems, our proposed PGA-Net distills high-level semantic features and replenishes low-level original information to reinforce module representation capability. Meanwhile, PGA-Net demonstrates notable training stability and superior performance even without pre-training. Extensive experiments demonstrate that our method consistently outperforms previous approaches even without pre-training, enabling thus an end-to-end model training from scratch. In COCO benchmark, PGA-Net consistently achieves over 3% improvements than the baseline (without pre-training) under various model configurations. 1