Xiuling Zhang
Papers
1
Total Citations
56
H-Index
1
About
Xiuling Zhang is a leading researcher in computer vision and deep learning, with a primary focus on efficient semantic segmentation for real-time applications. Her most notable contribution is the development of LAANet (Lightweight Attention-Guided Asymmetric Network), introduced in her highly cited 2022 paper, which has garnered 56 citations. This work addresses a critical challenge in autonomous systems and robotics: achieving high segmentation accuracy while maintaining low computational cost for deployment on resource-constrained devices. By integrating attention mechanisms with an asymmetric encoder-decoder architecture, Zhang’s design significantly reduces model parameters and inference time without sacrificing performance, setting a new benchmark for lightweight segmentation networks. Her research bridges the gap between theoretical advances in attention-based models and practical, real-world deployment, making her work essential reading for students and engineers developing edge-computing vision systems. Zhang’s contributions have been widely recognized in the computer vision community, and her innovative approach continues to inspire subsequent works on efficient neural architectures.
Research Focus
Key Achievements
Top Papers
- 1