M. Pawan Kumar

129 papers A* 25A 9B 1Journal 67Unranked 26
YearRankTypeTitle / Venue / Authors
2025 J jnl
AlphaEvolve: A coding agent for scientific and algorithmic discovery.
CoRR
Alexander Novikov, Ngân Vu, Marvin Eisenberger, Emilien Dupont, Po-Sen Huang, Adam Zsolt Wagner, Sergey Shirobokov, Borislav Kozlovskii, Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli, Matej Balog
2025 A* conf
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models.
ICLR
Francisco Eiras, Aleksandar Petrov, Philip Torr, M. Pawan Kumar, Adel Bibi
2024 A* conf
Efficient Error Certification for Physics-Informed Neural Networks.
ICML
Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy Dj Dvijotham, Philip Torr, M. Pawan Kumar
2024 A* conf
Expressive Losses for Verified Robustness via Convex Combinations.
ICLR
Alessandro De Palma, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio
2024 J jnl
Mathematical discoveries from program search with large language models.
Nat.
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, Alhussein Fawzi
2024 J jnl
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models.
CoRR
Francisco Eiras, Aleksandar Petrov, Philip H. S. Torr, M. Pawan Kumar, Adel Bibi
2024 J jnl
Scaling the Convex Barrier with Sparse Dual Algorithms.
J. Mach. Learn. Res.
Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar
2024 J jnl
Verified Neural Compressed Sensing.
CoRR
Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Alessandro De Palma, Robert Stanforth
2023 J jnl
Expressive Losses for Verified Robustness via Convex Combinations.
CoRR
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio
2023 J jnl
Faithful Knowledge Distillation.
CoRR
Tom A. Lamb, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, M. Pawan Kumar, Philip H. S. Torr, Francisco Eiras
2023 — conf
Neural Lower Bounds for Verification.
SaTML
Florian Jaeckle, M. Pawan Kumar
2023 J jnl
Provably Correct Physics-Informed Neural Networks.
CoRR
Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, Philip H. S. Torr, M. Pawan Kumar
2022 J jnl
A Stochastic Bundle Method for Interpolating Networks.
CoRR
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar
2022 J jnl
A Stochastic Bundle Method for Interpolation.
J. Mach. Learn. Res.
Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar
2022 J jnl
ANCER: Anisotropic Certification via Sample-wise Volume Maximization.
Trans. Mach. Learn. Res.
Francisco Eiras, Motasem Alfarra, Philip H. S. Torr, M. Pawan Kumar, Puneet K. Dokania, Bernard Ghanem, Adel Bibi
2022 J jnl
Faking Interpolation Until You Make It.
Trans. Mach. Learn. Res.
Alasdair Paren, Rudra P. K. Poudel, M. Pawan Kumar
2022 J jnl
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound.
CoRR
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth
2022 J jnl
In Defense of the Unitary Scalarization for Deep Multi-Task Learning.
CoRR
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar
2022 J jnl
Learning to be adversarially robust and differentially private.
CoRR
Jamie Hayes, Borja Balle, M. Pawan Kumar
2022 J jnl
Lookback for Learning to Branch.
CoRR
Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar
2022 J jnl
Lookback for Learning to Branch.
Trans. Mach. Learn. Res.
Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar
2021 J jnl
ANCER: Anisotropic Certification via Sample-wise Volume Maximization.
CoRR
Francisco Eiras, Motasem Alfarra, M. Pawan Kumar, Philip H. S. Torr, Puneet K. Dokania, Bernard Ghanem, Adel Bibi
2021 J jnl
Comment on Stochastic Polyak Step-Size: Performance of ALI-G.
CoRR
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2021 J jnl
Generating Adversarial Examples with Graph Neural Networks.
CoRR
Florian Jaeckle, M. Pawan Kumar
2021 A conf
Generating adversarial examples with graph neural networks.
UAI
Florian Jaeckle, M. Pawan Kumar
2021 J jnl
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition.
CoRR
Alessandro De Palma, Rudy Bunel, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2021 J jnl
Improving Local Effectiveness for Global robust training.
CoRR
Jingyue Lu, M. Pawan Kumar
2021 A* conf
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications.
NeurIPS
Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
2021 J jnl
Neural Network Branch-and-Bound for Neural Network Verification.
CoRR
Florian Jaeckle, Jingyue Lu, M. Pawan Kumar
2021 A* conf
Overcoming the Convex Barrier for Simplex Inputs.
NeurIPS
Harkirat Singh Behl, M. Pawan Kumar, Philip H. S. Torr, Krishnamurthy Dvijotham
2021 A* conf
Scaling the Convex Barrier with Active Sets.
ICLR
Alessandro De Palma, Harkirat S. Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar
2021 J jnl
Scaling the Convex Barrier with Sparse Dual Algorithms.
CoRR
Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar
2021 J jnl
Verifying Probabilistic Specifications with Functional Lagrangians.
CoRR
Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
2020 J jnl
Branch and Bound for Piecewise Linear Neural Network Verification.
J. Mach. Learn. Res.
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
2020 J jnl
Hybrid Models for Learning to Branch.
CoRR
Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio
2020 A conf
Lagrangian Decomposition for Neural Network Verification.
UAI
Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2020 J jnl
Lagrangian Decomposition for Neural Network Verification.
CoRR
Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2020 A* conf
Neural Network Branching for Neural Network Verification.
ICLR
Jingyue Lu, M. Pawan Kumar
2020 A* conf
Training Neural Networks for and by Interpolation.
ICML
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2020 — conf
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances.
ECCV (28)
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
2020 J jnl
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances.
CoRR
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
2019 — conf
A Statistical Approach to Assessing Neural Network Robustness.
ICLR (Poster)
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
2019 J jnl
Branch and Bound for Piecewise Linear Neural Network Verification.
CoRR
Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
2019 — conf
Deep Frank-Wolfe For Neural Network Optimization.
ICLR (Poster)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2019 A* conf
Dissimilarity Coefficient Based Weakly Supervised Object Detection.
CVPR
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
2019 J jnl
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials.
SIAM J. Imaging Sci.
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2019 J jnl
Linear programming-based submodular extensions for marginal estimation.
Comput. Vis. Image Underst.
Pankaj Pansari, Chris Russell, M. Pawan Kumar
2019 J jnl
Neural Network Branching for Neural Network Verification.
CoRR
Jingyue Lu, M. Pawan Kumar
2019 A* conf
New Convex Relaxations for MRF Inference With Unknown Graphs.
ICCV
Zhenhua Wang, Tong Liu, Qinfeng Shi, M. Pawan Kumar, Jianhua Zhang
2019 J jnl
Training Neural Networks for and by Interpolation.
CoRR
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018 J jnl
A Statistical Approach to Assessing Neural Network Robustness.
CoRR
Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar
2018 J jnl
Deep Frank-Wolfe For Neural Network Optimization.
CoRR
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018 J jnl
Dissimilarity Coefficient based Weakly Supervised Object Detection.
CoRR
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
2018 A* conf
Efficient Optimization for Rank-Based Loss Functions.
CVPR
Pritish Mohapatra, Michal Rolínek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar
2018 J jnl
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials.
CoRR
Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2018 A conf
Learning Human Poses from Actions.
BMVC
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
2018 J jnl
Learning Human Poses from Actions.
CoRR
Aditya Arun, C. V. Jawahar, M. Pawan Kumar
2018 A conf
Learning to Round for Discrete Labeling Problems.
AISTATS
Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar
2018 A conf
Optimal Submodular Extensions for Marginal Estimation.
AISTATS
Pankaj Pansari, Chris Russell, M. Pawan Kumar
2018 — conf
Smooth Loss Functions for Deep Top-k Classification.
ICLR (Poster)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018 J jnl
Smooth Loss Functions for Deep Top-k Classification.
CoRR
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018 J jnl
Worst-case Optimal Submodular Extensions for Marginal Estimation.
CoRR
Pankaj Pansari, Chris Russell, M. Pawan Kumar
2017 J jnl
Coplanar Repeats by Energy Minimization.
CoRR
James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
2017 A* conf
Efficient Linear Programming for Dense CRFs.
CVPR
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar
2017 — conf
Learning to superoptimize programs.
ICLR (Poster)
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
2017 J jnl
Piecewise Linear Neural Network verification: A comparative study.
CoRR
Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar
2017 A* conf
Truncated Max-of-Convex Models.
CVPR
Pankaj Pansari, M. Pawan Kumar
2017 — conf
Trusting SVM for Piecewise Linear CNNs.
ICLR (Poster)
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2016 J jnl
(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision.
Found. Trends Comput. Graph. Vis.
Nikos Komodakis, M. Pawan Kumar, Nikos Paragios
2016 A conf
Coplanar Repeats by Energy Minimization.
BMVC
James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum
2016 J jnl
DISCO Nets: DISsimilarity COefficient Networks.
CoRR
Diane Bouchacourt, M. Pawan Kumar, Sebastian Nowozin
2016 — conf
Efficient Continuous Relaxations for Dense CRF.
ECCV (2)
Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2016 J jnl
Efficient Continuous Relaxations for Dense CRF.
CoRR
Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
2016 J jnl
Efficient Linear Programming for Dense CRFs.
CoRR
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar
2016 J jnl
Efficient Optimization for Rank-based Loss Functions.
CoRR
Pritish Mohapatra, Michal Rolínek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar
2016 J jnl
Learning to superoptimize programs - Workshop Version.
CoRR
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
2016 J jnl
Learning to superoptimize programs.
CoRR
Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli
2016 — conf
Partial Linearization Based Optimization for Multi-class SVM.
ECCV (5)
Pritish Mohapatra, Puneet Kumar Dokania, C. V. Jawahar, M. Pawan Kumar
2016 J jnl
Rounding-based Moves for Semi-Metric Labeling.
J. Mach. Learn. Res.
M. Pawan Kumar, Puneet Kumar Dokania
2016 J jnl
Trusting SVM for Piecewise Linear CNNs.
CoRR
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2015 A* conf
Entropy-Based Latent Structured Output Prediction.
ICCV
Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar
2015 J jnl
Optimizing Average Precision Using Weakly Supervised Data.
IEEE Trans. Pattern Anal. Mach. Intell.
Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar
2015 J jnl
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation.
IEEE Trans. Pattern Anal. Mach. Intell.
M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller
2015 A* conf
Parsimonious Labeling.
ICCV
Puneet Kumar Dokania, M. Pawan Kumar
2015 J jnl
Parsimonious Labeling.
CoRR
Puneet Kumar Dokania, M. Pawan Kumar
2015 J jnl
Truncated Max-of-Convex Models.
CoRR
Pankaj Pansari, M. Pawan Kumar
2014 — conf
Efficient Optimization for Average Precision SVM.
NIPS
Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar
2014 — conf
Learning to Rank Using High-Order Information.
ECCV (4)
Puneet Kumar Dokania, Aseem Behl, C. V. Jawahar, M. Pawan Kumar
2014 A* conf
Optimizing Average Precision Using Weakly Supervised Data.
CVPR
Aseem Behl, C. V. Jawahar, M. Pawan Kumar
2014 — conf
Rounding-based Moves for Metric Labeling.
NIPS
M. Pawan Kumar
2013 — conf
Discriminative Parameter Estimation for Random Walks Segmentation.
MICCAI (3)
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
2013 J jnl
Discriminative Parameter Estimation for Random Walks Segmentation.
CoRR
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
2013 J jnl
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report.
CoRR
Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar
2013 — conf
Learning from M/EEG Data with Variable Brain Activation Delays.
IPMI
Wojciech Zaremba, M. Pawan Kumar, Alexandre Gramfort, Matthew B. Blaschko
2013 — book
Weakly Supervised Learning for Structured Output Prediction.
M. Pawan Kumar
2012 J jnl
A Buffer-Sizing Algorithm for Network-on-Chips with Multiple Voltage-Frequency Islands.
J. Electr. Comput. Eng.
Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli
2012 J jnl
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts
CoRR
M. Pawan Kumar, Daphne Koller
2012 A conf
Max-Margin Min-Entropy Models.
AISTATS
Kevin Miller, M. Pawan Kumar, Benjamin Packer, Danny Goodman, Daphne Koller
2012 J jnl
Modeling Latent Variable Uncertainty for Loss-based Learning
CoRR
M. Pawan Kumar, Benjamin Packer, Daphne Koller
2012 A* conf
Modeling Latent Variable Uncertainty for Loss-based Learning.
ICML
M. Pawan Kumar, Benjamin Packer, Daphne Koller
2011 — conf
A Method for Integrating Network-on-Chip Topologies with 3D ICs.
ISVLSI
M. Pawan Kumar, Anish S. Kumar, Srinivasan Murali, Luca Benini, Kamakoti Veezhinathan
2011 — conf
A Simulation Based Buffer Sizing Algorithm for Network on Chips.
ISVLSI
Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli
2011 J jnl
Improved Moves for Truncated Convex Models.
J. Mach. Learn. Res.
M. Pawan Kumar, Olga Veksler, Philip H. S. Torr
2011 A* conf
Learning specific-class segmentation from diverse data.
ICCV
M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller
2010 A* conf
Efficiently selecting regions for scene understanding.
CVPR
M. Pawan Kumar, Daphne Koller
2010 A* conf
Energy minimization for linear envelope MRFs.
CVPR
Pushmeet Kohli, M. Pawan Kumar
2010 J jnl
OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues.
IEEE Trans. Pattern Anal. Mach. Intell.
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2010 — conf
Self-Paced Learning for Latent Variable Models.
NIPS
M. Pawan Kumar, Benjamin Packer, Daphne Koller
2009 J jnl
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs.
J. Mach. Learn. Res.
M. Pawan Kumar, Vladimir Kolmogorov, Philip H. S. Torr
2009 A* conf
Efficient discriminative learning of parts-based models.
ICCV
M. Pawan Kumar, Andrew Zisserman, Philip H. S. Torr
2009 — conf
Learning a Small Mixture of Trees.
NIPS
M. Pawan Kumar, Daphne Koller
2009 A conf
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts.
UAI
M. Pawan Kumar, Daphne Koller
2009 J jnl
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions.
IEEE Trans. Pattern Anal. Mach. Intell.
Pushmeet Kohli, M. Pawan Kumar, Philip H. S. Torr
2008 A* conf
Efficiently solving convex relaxations for MAP estimation.
ICML
M. Pawan Kumar, Philip H. S. Torr
2008 — conf
Improved Moves for Truncated Convex Models.
NIPS
M. Pawan Kumar, Philip H. S. Torr
2008 J jnl
Learning Layered Motion Segmentations of Video.
Int. J. Comput. Vis.
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2007 A* conf
An Invariant Large Margin Nearest Neighbour Classifier.
ICCV
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2007 A* conf
P3 & Beyond: Solving Energies with Higher Order Cliques.
CVPR
Pushmeet Kohli, M. Pawan Kumar, Philip H. S. Torr
2006 — conf
An Object Category Specific mrffor Segmentation.
Toward Category-Level Object Recognition
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2006 — conf
Fast Memory-Efficient Generalized Belief Propagation.
ECCV (4)
M. Pawan Kumar, Philip H. S. Torr
2006 — conf
Learning Class-Specific Edges for Object Detection and Segmentation.
ICVGIP
Mukta Prasad, Andrew Zisserman, Andrew W. Fitzgibbon, M. Pawan Kumar, Philip H. S. Torr
2006 — conf
Solving Markov Random Fields using Second Order Cone Programming Relaxations.
CVPR (1)
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2005 A* conf
Learning Layered Motion Segmentation of Video.
ICCV
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2005 — conf
OBJ CUT.
CVPR (1)
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2004 B conf
Building blocks for autonomous navigation using contour correspondences.
ICIP
M. Pawan Kumar, C. V. Jawahar, P. J. Narayanan
2004 J jnl
Discrete contours in multiple views: approximation and recognition.
Image Vis. Comput.
M. Pawan Kumar, Saurabh Goyal, Sujit Kuthirummal, C. V. Jawahar, P. J. Narayanan
2004 A conf
Extending Pictorial Structures for Object Recognition.
BMVC
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2004 — conf
Learning Layered Pictorial Structures from Video.
ICVGIP
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman
2002 — conf
Polygonal Approximation of Closed Curves across Multiple Views.
ICVGIP
M. Pawan Kumar, Saurabh Goyal, C. V. Jawahar, P. J. Narayanan