LAPX: Lightweight Hourglass Network with Global Context
Haopeng Zhao, Marsha Mariya Kappan, Mahdi Bamdad, Francisco Cruz

TL;DR
LAPX is a lightweight hourglass network with self-attention that captures global context, achieving competitive human pose estimation accuracy on benchmarks with minimal parameters and real-time performance for edge deployment.
Contribution
The paper introduces LAPX, a novel lightweight hourglass network with integrated self-attention and refined modules, optimized for efficient edge-device human pose estimation.
Findings
Achieves competitive accuracy on MPII and COCO datasets.
Uses only 2.3 million parameters.
Operates in real-time on edge devices.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHuman Pose and Action Recognition · Gait Recognition and Analysis · Robot Manipulation and Learning
