Minghao Zhao

Orcid: 0000-0002-8062-4571

Affiliations:
  • Jilin University, College of Computer Science and Technology, Changchun, China


According to our database1, Minghao Zhao authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Efficient Vision Transformer With Token-Selective and Merging Strategies for Autonomous Underwater Vehicles.
IEEE Internet Things J., October, 2024

A Feature-Enhanced and Adaptive Routing Framework for Fish School Detection on AUVs for Degraded Underwater Imaging Environments.
IEEE Internet Things J., May, 2024

Event-Based Low-Illumination Image Enhancement.
IEEE Trans. Multim., 2024

RNVE: A Real Nighttime Vision Enhancement Benchmark and Dual-Stream Fusion Network.
IEEE Signal Process. Lett., 2024

2023
An Underwater Human-Robot Interaction Using a Visual-Textual Model for Autonomous Underwater Vehicles.
Sensors, 2023

Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition.
Proceedings of the IEEE International Conference on Multimedia and Expo, 2023

2022
A Multi-AUV Maritime Target Search Method for Moving and Invisible Objects Based on Multi-Agent Deep Reinforcement Learning.
Sensors, 2022

Skeleton-based Action Recognition via Temporal-Channel Aggregation.
CoRR, 2022

2021
Diver's hand gesture recognition and segmentation for human-robot interaction on AUV.
Signal Image Video Process., 2021

A Method for Underwater Human-Robot Interaction Based on Gestures Tracking with Fuzzy Control.
Int. J. Fuzzy Syst., 2021

Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer.
Proceedings of the Knowledge Science, Engineering and Management, 2021

2019
A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU.
J. Supercomput., 2019

Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network.
Sensors, 2019

Simulation of Evacuating Crowd Based on Deep Learning and Social Force Model.
IEEE Access, 2019


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