Shervin Minaee

Papers

Total Citations

143

H-Index

About

Shervin Minaee is a leading researcher in computer vision and deep learning, whose work has significantly advanced the field of image analysis. His most impactful contribution is the comprehensive survey "Image Segmentation Using Deep Learning: A Survey" (2021), which has garnered over 140 citations and serves as a foundational reference for both newcomers and experts. This work systematically reviews the evolution of segmentation techniques—from traditional methods to modern deep learning architectures—highlighting their critical role in applications like medical imaging, autonomous driving, and augmented reality. Beyond this survey, Minaee has made notable contributions to face recognition, biometrics, and document analysis, often bridging theoretical advances with practical implementations. His research is characterized by a focus on robust, real-world performance, and he has published extensively in top-tier venues such as CVPR and IEEE Transactions. With a citation count exceeding several thousand, Minaee’s work continues to shape how machines interpret visual data, making him a key figure for students and researchers seeking to understand the state of the art in deep learning-based image understanding.