Paul Fieguth

Papers

Total Citations

229

H-Index

About

Paul Fieguth is a leading researcher in computer vision and machine learning, with a particular focus on deep learning for visual information retrieval. His major contributions center on advancing the field of instance retrieval—the task of searching large databases for specific visual content. Fieguth’s most influential work, the comprehensive survey "Deep Learning for Instance Retrieval: A Survey" (2022), has garnered over 174 citations, establishing itself as a key reference for researchers and practitioners. This survey systematically reviews deep learning architectures, training strategies, and evaluation metrics, providing a roadmap for tackling challenges in domains ranging from social media to medical imaging and robotics. A related earlier survey (2021) further solidified his impact, with over 51 citations. By synthesizing a rapidly evolving field and identifying open problems, Fieguth has helped shape the direction of modern image retrieval research. His work is essential reading for students and researchers seeking to understand how deep learning enables efficient, scalable search across massive visual datasets, making him a pivotal figure in the intersection of computer vision and applied machine learning.