ACENet: Attention-Driven Contextual Features-Enhanced Lightweight EfficientNet for 2D Hand Pose Estimation-Reference-Cited by-同舟云学术

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ACENet: Attention-Driven Contextual Features-Enhanced Lightweight EfficientNet for 2D Hand Pose Estimation

Published:2023-11-29 Issue: Volume: Page:
ISSN:
Container-title:2023 38th International Conference on Image and Vision Computing New Zealand (IVCNZ)
language:
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Author:

Salman Sartaj Ahmed¹,Zakir Ali¹,Benitez-Garcia Gibran¹,Takahashi Hiroki²

Affiliation:

1. The University of Electro-Communications,Graduate School of Informatics and Engineering,Tokyo,Japan

2. The University of Electro-Communications,Graduate School of Informatics and Engineering Artificial Intelligence Exploration/Meta-Networking Research Center,Tokyo,Japan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10343468/10343547/10343701.pdf?arnumber=10343701

Reference26 articles.

1. Improving articulated hand pose detection for static finger sign recognition in RGB-D images

2. New intelligent interface study based on K-means gaze tracking

3. Multi-sensor system for driver's hand-gesture recognition

4. Hand pose estimation in object-interaction based on deep learning for virtual reality applications

5. Static Hand Gesture Recognition with Parallel CNNs for Space Human-Robot Interaction

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