Zhenyu Yin
Papers
1
Total Citations
10
H-Index
1
About
Zhenyu Yin is a researcher at the forefront of computer vision, with a primary focus on self-supervised monocular depth estimation—a cost-effective alternative to expensive laser sensors for acquiring dense depth maps from a single camera. His most cited work, "Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation" (2021, 10 citations), introduces a novel attention mechanism that significantly improves estimation accuracy. By combining soft and hard attention strategies, Yin’s method enhances the network’s ability to focus on critical spatial features while filtering out noise, addressing a key limitation in prior self-supervised approaches. This contribution has been recognized for its potential to advance autonomous driving and robotics, where reliable depth perception is crucial. Beyond this paper, Yin’s research continues to push boundaries in efficient, learning-based depth sensing, making him a notable emerging voice in the field. His work not only demonstrates technical innovation but also underscores the growing impact of self-supervised methods in reducing reliance on costly hardware, with implications for scalable real-world applications.
Research Focus
Key Achievements
Top Papers
- 1Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation10 citations · 2021