Longuet-Higgins Prize

The annual Longuet-Higgins prize is presented by the IEEE Pattern Analysis and Machine Intelligence (PAMI) Technical Committee at each year’s CVPR for fundamental contributions in computer vision. The award recognizes CVPR papers from ten years ago with significant impact on computer vision research. The prize is named after theoretical chemist and cognitive scientist H. Christopher Longuet-Higgins. Winners are decided by a committee appointed by the TCPAMI Awards Committee.

Beginning this year, the Awards Committee is accepting nominations for papers to be considered for the Longuet-Higgins prize. With the continued growth of our research community, including the number of papers appearing at CVPR each year, the committee feels that opening up the process for nominations will improve the selection process and reduce the chances of missing a worthy paper. Please note that a paper does NOT need an external nomination to be considered for the award. The selection committee will still conduct its usual process, with nominated papers added to the pool under consideration. Nominations are particularly encouraged for papers where the original version first appeared at CVPR but a better-known and more commonly cited version was subsequently published in journal form, making the full impact of the CVPR paper less obvious.

Nominations should include the title of the paper with a brief explanation of any non-obvious impact. Nominations should be submitted by the deadline set by the PAMI TC Chair each year to pami.awards@gmail.com.

Previous Awardees

2023 “Online Object Tracking: A Benchmark” Y. Wu, J. Lim, M.-H. Yang
2022 “Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite” A. Geiger, P. Lenz, R. Urtasun
2021 “Real-time human pose recognition in parts from single depth image” J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake
2021 “Baby talk: Understanding and generating simple image descriptions” G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, T. L. Berg
2020 “Secrets of Optical Flow Estimation and Their Principles” D. Sun, S. Roth, M. Black
2019 “ImageNet: A large-scale hierarchical image database” J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei
2018 “A Discriminatively Trained, Multiscale, Deformable Part Model” P. Felzenszwalb, D. McAllester, and D. Ramanan
2017 “Accurate, Dense, and Robust Multi-View Stereopsis” Y. Furukawa, J. Ponce
2017 “Object Retrieval with Large Vocabularies and Fast Spatial Matching” J. Philbin, O. Chum, M. Isard, J. Sivic, A. Zisserman
2016 “Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories” S. Lazebnik, C. Schmid, J. Ponce
2016 “Scalable Recognition with a Vocabulary Tree” D. Nister and H. Stewenius
2015 “Histograms of oriented gradients for human detection” N. Dalal, B. Triggs
2015 “A non-local algorithm for image denoising” A. Buades, B. Coll, J.-M. Morel
2014 “A performance evaluation of local descriptors” K. Mikolajczyk, C. Schmid
2013 “Object Class Recognition by Unsupervised Scale-Invariant Learning” R. Fergus, P. Perona, A. Zisserman
2011 “Rapid Object Detection using a Boosted Cascade of Simple Features” P. A. Viola, M. J. Jones
2010 “Efficient Matching of Pictorial Structures” P. F. Felzenszwalb, D. P. Huttenlocher
2010 “Real-Time Tracking of Non-Rigid Objects Using Mean Shift” D. Comaniciu, V. Ramesh, P. Meer
2009 “Statistics of Natural Images and Models” J. Huang, D. Mumford
2009 “Adaptive Background Mixture Models for Real-Time Tracking” C. Stauffer, W. E. L. Grimson
2008 “Probabilistic modeling of local appearance and spatial relationships for object recognition” H. Schneiderman and T. Kanade
2008 “Tracking people with twists and exponential maps” C. Bregler and J. Malik
2007 “Normalized Cuts and Image Segmentation” J. Shi, J. Malik
2007 “Training Support Vector Machines: An Application to Face Detection” E. Osuna, R. Freund, F. Girosi
2006 “Neural Network-Based Face Detection” H. Rowley, S. Baluja, T. Kanade
2006 “Combining greyvalue invariants with local constraints for object recognition” C. Schmid, R. Mohr
2005 “Boundary detection by minimizing functionals” D. Mumford, J. Shah
2005 “Layered representation for motion analysis” T. Adelson, J. Wang