About
Contact & Profiles
A5000952257
Research Areas
- Context-Aware Activity Recognition Systems
- Plant Pathogens and Resistance
- Non-Invasive Vital Sign Monitoring
- IoT and Edge/Fog Computing
- Fungal Plant Pathogen Control
- Human Pose and Action Recognition
- Plant Pathogens and Fungal Diseases
Nanjing Normal University
2024
Anhui Agricultural University
2016
Early-exiting has recently provided an ideal solution for accelerating activity inference by attaching internal classifiers to deep neural networks. It allows easy samples be predicted at shallower layers, without executing deeper hence leading notable adaptiveness in terms of accuracy-speed trade-off under varying resource demands. However, prior most works typically optimize all the equally on types data. As a result, will only see hard during test phase, which renders model suboptimal due...
10.1109/tnsre.2024.3457830
article
EN
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IEEE Transactions on Neural Systems and Rehabilitation Engineering
2024-01-01
10.1094/pdis-07-15-0815-pdn
article
EN
other-oa
Plant Disease
2016-01-06
10.1109/jsen.2024.3510811
article
EN
IEEE Sensors Journal
2024-01-01
10.1109/jsen.2024.3412736
article
EN
IEEE Sensors Journal
2024-06-17
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