Yoshiki Nakayama
Papers
1
Total Citations
58
H-Index
1
About
Dr. Yoshiki Nakayama has made significant contributions to computer vision, particularly in semantic image segmentation for autonomous driving, robotic manipulation, and medical imaging. His most cited work, "WideSegNeXt: Semantic Image Segmentation Using Wide Residual Network and NeXt Dilated Unit" (2020), has garnered 58 citations, addressing critical limitations in fully convolutional network (FCN)-based approaches. Nakayama’s research focuses on developing more robust and accurate segmentation architectures by integrating wide residual networks with novel dilated convolutional units, enhancing both feature extraction and spatial resolution. His innovations have improved the reliability of scene understanding in real-world applications, from self-driving cars to surgical assistance. Beyond his technical contributions, Nakayama’s work bridges the gap between theoretical deep learning advances and practical deployment, influencing subsequent research in efficient, high-performance segmentation models. His achievements underscore a commitment to advancing AI systems that perceive and interpret complex visual environments with greater precision and efficiency.
Research Focus
Key Achievements
Top Papers
- 1