Zhencheng Ye

East China University of Science and Technology

Papers

Total Citations

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About

Zhencheng Ye is a leading researcher in computer vision and autonomous driving, with a focus on scene understanding through deep learning. His work bridges semantic segmentation and depth completion—two critical tasks for enabling machines to perceive 3D environments. In his highly cited 2021 paper, "Multitask GANs for Semantic Segmentation and Depth Completion With Cycle Consistency" (44 citations), Ye introduced a novel multitask learning framework that leverages generative adversarial networks and cycle-consistency constraints. This approach not only improves accuracy in both tasks simultaneously but also reduces the need for expensive labeled data, making it highly practical for real-world robotics and autonomous driving systems. Beyond this, Ye's contributions have advanced the integration of multimodal perception, demonstrating how complementary tasks can reinforce each other. His research has been recognized for its impact on efficient, robust scene understanding, earning citations from peers developing next-generation autonomous systems. Ye continues to push boundaries in deep learning for perception, inspiring students and researchers to explore synergistic multitask architectures.