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Related Experiment Video

Updated: Oct 15, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

InterNet+: A Light Network for Hand Pose Estimation.

Yang Liu¹, Jie Jiang¹, Jiahao Sun¹

¹College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Sensors (Basel, Switzerland)

|October 26, 2021

Summary

This summary is machine-generated.

This study enhances hand pose estimation from RGB images by introducing a novel feature extractor. The improved network architecture achieves greater accuracy compared to existing methods like InterNet.

Keywords:

attention mechanism hand pose estimation neural network

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Area of Science:

Computer Vision
Machine Learning

Background:

Hand pose estimation from RGB images is challenging due to missing depth information.
Previous methods like InterNet have shown promise but require further improvement.

Purpose of the Study:

To enhance the accuracy of hand pose estimation from RGB images.
To introduce a novel feature extractor incorporating recent computer vision advancements.

Main Methods:

Redesigned a feature extractor based on MobileNet v3 and MoGA architectures.
Integrated advanced components like the ACON activation function and attention mechanisms.
Developed a new network architecture for improved global feature extraction.

Main Results:

The proposed network demonstrates superior performance in hand pose estimation.
Achieved greater accuracy compared to InterNet and other similar networks.
Effective extraction of global features from RGB hand images.

Conclusions:

The redesigned feature extractor significantly improves hand pose estimation accuracy.
The integration of novel computer vision techniques offers a promising direction for future research.
The developed network provides a more robust solution for RGB-based hand pose estimation.