Haobo Yuan

Papers

Total Citations

203

H-Index

About

Haobo Yuan is a leading researcher in computer vision, with a primary focus on visual segmentation—the task of partitioning images, video frames, or point clouds into meaningful segments. His most impactful work is the comprehensive survey "Transformer-Based Visual Segmentation: A Survey" (2024), which has garnered 192 citations in under a year. This seminal paper systematically reviews the transformative role of Transformer architectures in segmentation tasks, covering applications from autonomous driving and medical analysis to robot sensing and image editing. By synthesizing the rapid advancements in deep learning-based segmentation methods, Yuan provides a critical roadmap for researchers navigating this evolving field. His earlier 2023 version of the survey (11 citations) laid the groundwork for this expanded analysis. Yuan’s contributions are particularly notable for bridging the gap between traditional convolutional approaches and modern Transformer-based models, offering both theoretical insights and practical guidance. His work has become an essential reference for students and practitioners seeking to understand state-of-the-art segmentation techniques, cementing his reputation as a key synthesizer and innovator in visual AI.