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
| 2024 | “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation” | R. Girshick, J. Donahue, T. Darrel, J. Malik |
| 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 |