Mao Ye

University of Electronic Science and Technology of China, Murdoch University

Papers

Total Citations

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About

Dr. Mao Ye is a computer vision and robotics researcher whose work spans autonomous navigation, robotic control, and human action recognition. His most impactful contribution is a road segmentation method for all-day outdoor robot navigation (2018, 59 citations), which addresses a critical challenge in field robotics by enabling reliable visual perception under varying lighting conditions. In robotic systems, Dr. Ye has developed discrete-time integral terminal sliding mode control for robotic fish (2021, 15 citations), advancing speed tracking in bio-inspired underwater vehicles. His research also explores deep learning for visual understanding, including a multi-task network that learns saliency features for joint face detection and recognition (2016, 11 citations), and an improved attention-based spatiotemporal-stream model for video action recognition (2020, 7 citations). This latter work leverages attention mechanisms to determine not only where but *when* to focus, extracting discriminative spatial and temporal features. Dr. Ye’s work demonstrates a consistent thread of applying intelligent algorithms—from sliding mode control to attention networks—to solve real-world perception and control problems, making contributions that are cited across robotics and computer vision communities.