轻量级人体姿态估计模型，兼顾精度与边缘部署效率。
LAPX: Lightweight Hourglass Network with Global Context
- 基于自注意力机制捕捉全局上下文信息，优化网络结构。
- 仅用230万参数，在MPII和COCO上达到领先精度。
- 适合边缘设备实时推理，兼顾速度与准确率。
人体姿态估计是计算机视觉中的关键任务。现有最先进方法虽精度高，但参数量大、计算成本高。诸多轻量化变体虽降低模型规模与开销，但仍存在不适合边缘设备部署的问题。而过度简化设计的模型往往因精度受限。为此，本文提出LAPX，一种基于LAP改进的轻量级环状网络，引入自注意力模块以捕获全局上下文信息，并优化层级设计与轻量化注意力模块。该模型在MPII和COCO两个基准数据集上取得优异性能，仅含230万参数，且实现实时推理，验证了其在边缘设备上的适用性。
原文摘要 · Abstract (English)
Human pose estimation is a crucial task in computer vision. Methods that have SOTA (State-of-the-Art) accuracy, often involve a large number of parameters and incur substantial computational cost. Many lightweight variants have been proposed to reduce the model size and computational cost of them. However, several of these methods still contain components that are not well suited for efficient deployment on edge devices. Moreover, models that primarily emphasize inference speed on edge devices often suffer from limited accuracy due to their overly simplified designs. To address these limitations, we propose LAPX, an Hourglass network with self-attention that captures global contextual information, based on previous work, LAP. In addition to adopting the self-attention module, LAPX advances the stage design and refine the lightweight attention modules. It achieves competitive results on two benchmark datasets, MPII and COCO, with only 2.3M parameters, and demonstrates real-time performance, confirming its edge-device suitability.
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