AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
CL-HPE is presented, a lightweight framework for competitive and efficient pose estimation that provides a practical solution in resource-constrained environments and suggests a favorable accuracy–efficiency trade-off among lightweight pose estimators.
An HGDS-YOLOv8-Pose algorithm embedded with HSAN, GL-CRM, DySample and SCAM modules for robust outdoor human pose estimation, outperforming competing methods including KAPAO-s and YOLOv8n-Pose.
This paper proposes a lightweight high-resolution pose estimation network, UG-HRNet, which significantly reduces model complexity while maintaining competitive performance, serving as a practical alternative to existing popular lightweight networks.
This paper proposes MFA-Pose, an improved YOLO11s-Pose framework that enhances contextual representation and cross-scale feature reconstruction that provides a favorable balance between pose estimation accuracy and model complexity for industrial surveillance applications.
The method demonstrates the feasibility of a lightweight dual-attention synergistic mechanism that significantly improves vertical boundary localization at the cost of a slight decrease in general detection accuracy, providing a new approach for fine-grained measurement in structured scenarios with a fixed monocular ca...
Existing head pose estimation (HPE) methods focus on utilizing the information of one single image and ignore cross-viewpoint pose consistency. To remedy this, we propose a dual-branch self-correcting pose consistency network (DSPC-Net) for HPE. The idea is to construct flipped image pairs as intrinsic pose constraints...
This work proposes a novel attention-free integrated spatiotemporal architecture named Integrated Mamba-HyperGCN (Pose Magic++), which significantly improves the estimation accuracy of peripheral joints without incurring extra computational cost.
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