The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Leonid Sigal sits on this spectrum.
This scientist: 211 publications — 50th percentile
50% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Leonid Sigal sits on this spectrum.
This scientist: 60 D-Index — 78th percentile
78% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Leonid Sigal is affiliated with the University of British Columbia in Canada. Their research primarily falls within the field of Computer Science, with a strong focus on Computer Vision and Pattern Recognition. They have contributed extensively to topics such as Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning, Advanced Image and Video Retrieval Techniques, Generative Adversarial Networks and Image Synthesis, Advanced Vision and Imaging, Human Pose and Action Recognition, and Video Analysis and Summarization.
Their work includes papers published in various venues, reflecting a multidisciplinary engagement with both conferences and journals. Recent publications include:
Leonid Sigal has frequently published in venues such as arXiv (Cornell University), SSRN Electronic Journal, Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, and the International Journal of Computer Vision. The arXiv platform hosts the majority of their work, with over 50 publications there.
Collaboration is a significant aspect of their research. Frequent co-authors include Raghav Goyal, Aditya Chinchure, James J. Little, Carlos Esteves, and Ameesh Makadia, with multiple joint publications indicating ongoing research partnerships.
Their body of work spans both theoretical and applied domains, focusing on advancing methods in visual understanding and synthesis through machine learning techniques. This includes adapting models to work efficiently with limited data and improving the interpretability and performance of vision-based AI systems across various tasks.
Leonid Sigal;Alexandru O. Balan;Michael J. Black
L. Sigal;S. Bhatia;S. Roth;M.J. Black
Leonid Sigal;Michael J. Black
Hedvig Sidenbladh;Michael J. Black;Leonid Sigal
Shugao Ma;Leonid Sigal;Stan Sclaroff
Zitian Chen;Yanwei Fu;Yinda Zhang;Yu-Gang Jiang
L. Sigal;S. Sclaroff;V. Athitsos
A.O. Balan;L. Sigal;M.J. Black;J.E. Davis
Makoto Yamada;Wittawat Jitkrittum;Leonid Sigal;Eric P. Xing
L. Sigal;M.J. Black
Michalis Raptis;Leonid Sigal
Huijuan Xu;Kun He;Bryan A. Plummer;Leonid Sigal
R. Rosales;V. Athitsos;L. Sigal;S. Sclaroff
Tian Lan;Leonid Sigal;Greg Mori
A.O. Balan;L. Sigal;M.J. Black
L. Sigal;S. Sclaroff;V. Athitsos
Leonid Sigal;Alexandru Balan;Michael J. Black
Takaaki Shiratori;Hyun Soo Park;Leonid Sigal;Yaser Sheikh
Bo Zhao;Lili Meng;Weidong Yin;Leonid Sigal
Gunhee Kim;Leonid Sigal;Eric P. Xing
Peyman Bateni;Raghav Goyal;Vaden Masrani;Frank Wood
Thomas B. Moeslund;Adrian Hilton;Volker Krüger;Leonid Sigal
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Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Publications: 30
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