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2020 – today
- 2024
[j20]Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar:
Scaling the Convex Barrier with Sparse Dual Algorithms. J. Mach. Learn. Res. 25: 61:1-61:51 (2024)- [j19]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:
Mathematical discoveries from program search with large language models. Nat. 625(7995): 468-475 (2024) - [c60]Alessandro De Palma, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio:
Expressive Losses for Verified Robustness via Convex Combinations. ICLR 2024 - [c59]Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy Dj Dvijotham, Philip Torr, M. Pawan Kumar:
Efficient Error Certification for Physics-Informed Neural Networks. ICML 2024 - [i46]Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Alessandro De Palma, Robert Stanforth:
Verified Neural Compressed Sensing. CoRR abs/2405.04260 (2024) - [i45]Francisco Eiras, Aleksandar Petrov, Philip H. S. Torr, M. Pawan Kumar, Adel Bibi:
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models. CoRR abs/2406.10288 (2024) - 2023
- [c58]Florian Jaeckle, M. Pawan Kumar:
Neural Lower Bounds for Verification. SaTML 2023: 524-536 - [i44]Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, Philip H. S. Torr, M. Pawan Kumar:
Provably Correct Physics-Informed Neural Networks. CoRR abs/2305.10157 (2023) - [i43]Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth, Alessio Lomuscio:
Expressive Losses for Verified Robustness via Convex Combinations. CoRR abs/2305.13991 (2023) - [i42]Tom A. Lamb, Rudy Bunel, Krishnamurthy (Dj) Dvijotham, M. Pawan Kumar, Philip H. S. Torr, Francisco Eiras:
Faithful Knowledge Distillation. CoRR abs/2306.04431 (2023) - 2022
- [j18]Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar:
A Stochastic Bundle Method for Interpolation. J. Mach. Learn. Res. 23: 15:1-15:57 (2022) - [j17]Francisco Eiras, Motasem Alfarra, Philip H. S. Torr, M. Pawan Kumar, Puneet K. Dokania, Bernard Ghanem, Adel Bibi:
ANCER: Anisotropic Certification via Sample-wise Volume Maximization. Trans. Mach. Learn. Res. 2022 (2022) - [j16]Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar:
Lookback for Learning to Branch. Trans. Mach. Learn. Res. 2022 (2022) - [j15]Alasdair Paren, Rudra P. K. Poudel, M. Pawan Kumar:
Faking Interpolation Until You Make It. Trans. Mach. Learn. Res. 2022 (2022) - [i41]Jamie Hayes, Borja Balle, M. Pawan Kumar:
Learning to be adversarially robust and differentially private. CoRR abs/2201.02265 (2022) - [i40]Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, M. Pawan Kumar:
In Defense of the Unitary Scalarization for Deep Multi-Task Learning. CoRR abs/2201.04122 (2022) - [i39]Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar:
A Stochastic Bundle Method for Interpolating Networks. CoRR abs/2201.12678 (2022) - [i38]Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar, Robert Stanforth:
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound. CoRR abs/2206.14772 (2022) - [i37]Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar:
Lookback for Learning to Branch. CoRR abs/2206.14987 (2022) - 2021
- [c57]Alessandro De Palma, Harkirat S. Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar:
Scaling the Convex Barrier with Active Sets. ICLR 2021 - [c56]Harkirat Singh Behl, M. Pawan Kumar, Philip H. S. Torr, Krishnamurthy Dvijotham:
Overcoming the Convex Barrier for Simplex Inputs. NeurIPS 2021: 4871-4882 - [c55]Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar:
Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications. NeurIPS 2021: 11136-11147 - [c54]Florian Jaeckle, M. Pawan Kumar:
Generating adversarial examples with graph neural networks. UAI 2021: 1556-1564 - [i36]Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar:
Scaling the Convex Barrier with Sparse Dual Algorithms. CoRR abs/2101.05844 (2021) - [i35]Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar:
Verifying Probabilistic Specifications with Functional Lagrangians. CoRR abs/2102.09479 (2021) - [i34]Alessandro De Palma, Rudy Bunel, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition. CoRR abs/2104.06718 (2021) - [i33]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Comment on Stochastic Polyak Step-Size: Performance of ALI-G. CoRR abs/2105.10011 (2021) - [i32]Florian Jaeckle, M. Pawan Kumar:
Generating Adversarial Examples with Graph Neural Networks. CoRR abs/2105.14644 (2021) - [i31]Francisco Eiras, Motasem Alfarra, M. Pawan Kumar, Philip H. S. Torr, Puneet K. Dokania, Bernard Ghanem, Adel Bibi:
ANCER: Anisotropic Certification via Sample-wise Volume Maximization. CoRR abs/2107.04570 (2021) - [i30]Florian Jaeckle, Jingyue Lu, M. Pawan Kumar:
Neural Network Branch-and-Bound for Neural Network Verification. CoRR abs/2107.12855 (2021) - [i29]Jingyue Lu, M. Pawan Kumar:
Improving Local Effectiveness for Global robust training. CoRR abs/2110.14030 (2021) - 2020
- [j14]Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Branch and Bound for Piecewise Linear Neural Network Verification. J. Mach. Learn. Res. 21: 42:1-42:39 (2020) - [c53]Aditya Arun, C. V. Jawahar, M. Pawan Kumar:
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances. ECCV (28) 2020: 254-270 - [c52]Jingyue Lu, M. Pawan Kumar:
Neural Network Branching for Neural Network Verification. ICLR 2020 - [c51]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Training Neural Networks for and by Interpolation. ICML 2020: 799-809 - [c50]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. UAI 2020: 370-379 - [i28]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. CoRR abs/2002.10410 (2020) - [i27]Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio:
Hybrid Models for Learning to Branch. CoRR abs/2006.15212 (2020) - [i26]Aditya Arun, C. V. Jawahar, M. Pawan Kumar:
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances. CoRR abs/2007.09397 (2020)
2010 – 2019
- 2019
- [j13]Pankaj Pansari, Chris Russell, M. Pawan Kumar:
Linear programming-based submodular extensions for marginal estimation. Comput. Vis. Image Underst. 189 (2019) - [j12]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12(1): 287-318 (2019) - [c49]Aditya Arun, C. V. Jawahar, M. Pawan Kumar:
Dissimilarity Coefficient Based Weakly Supervised Object Detection. CVPR 2019: 9432-9441 - [c48]Zhenhua Wang, Tong Liu, Qinfeng Shi, M. Pawan Kumar, Jianhua Zhang:
New Convex Relaxations for MRF Inference With Unknown Graphs. ICCV 2019: 9934-9942 - [c47]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Deep Frank-Wolfe For Neural Network Optimization. ICLR (Poster) 2019 - [c46]Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar:
A Statistical Approach to Assessing Neural Network Robustness. ICLR (Poster) 2019 - [i25]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Training Neural Networks for and by Interpolation. CoRR abs/1906.05661 (2019) - [i24]Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Branch and Bound for Piecewise Linear Neural Network Verification. CoRR abs/1909.06588 (2019) - [i23]Jingyue Lu, M. Pawan Kumar:
Neural Network Branching for Neural Network Verification. CoRR abs/1912.01329 (2019) - 2018
- [c45]Pankaj Pansari, Chris Russell, M. Pawan Kumar:
Optimal Submodular Extensions for Marginal Estimation. AISTATS 2018: 327-335 - [c44]Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar:
Learning to Round for Discrete Labeling Problems. AISTATS 2018: 1047-1056 - [c43]Aditya Arun, C. V. Jawahar, M. Pawan Kumar:
Learning Human Poses from Actions. BMVC 2018: 254 - [c42]Pritish Mohapatra, Michal Rolínek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar:
Efficient Optimization for Rank-Based Loss Functions. CVPR 2018: 3693-3701 - [c41]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Smooth Loss Functions for Deep Top-k Classification. ICLR (Poster) 2018 - [i22]Pankaj Pansari, Chris Russell, M. Pawan Kumar:
Worst-case Optimal Submodular Extensions for Marginal Estimation. CoRR abs/1801.06490 (2018) - [i21]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Smooth Loss Functions for Deep Top-k Classification. CoRR abs/1802.07595 (2018) - [i20]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials. CoRR abs/1805.09028 (2018) - [i19]Aditya Arun, C. V. Jawahar, M. Pawan Kumar:
Learning Human Poses from Actions. CoRR abs/1807.09075 (2018) - [i18]Stefan Webb, Tom Rainforth, Yee Whye Teh, M. Pawan Kumar:
A Statistical Approach to Assessing Neural Network Robustness. CoRR abs/1811.07209 (2018) - [i17]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Deep Frank-Wolfe For Neural Network Optimization. CoRR abs/1811.07591 (2018) - [i16]Aditya Arun, C. V. Jawahar, M. Pawan Kumar:
Dissimilarity Coefficient based Weakly Supervised Object Detection. CoRR abs/1811.10016 (2018) - 2017
- [c40]Pankaj Pansari, M. Pawan Kumar:
Truncated Max-of-Convex Models. CVPR 2017: 664-672 - [c39]Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar:
Efficient Linear Programming for Dense CRFs. CVPR 2017: 2934-2942 - [c38]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Trusting SVM for Piecewise Linear CNNs. ICLR (Poster) 2017 - [c37]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs. ICLR (Poster) 2017 - [i15]Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Piecewise Linear Neural Network verification: A comparative study. CoRR abs/1711.00455 (2017) - [i14]James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum:
Coplanar Repeats by Energy Minimization. CoRR abs/1711.09432 (2017) - 2016
- [j11]Nikos Komodakis, M. Pawan Kumar, Nikos Paragios:
(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision. Found. Trends Comput. Graph. Vis. 10(1): 1-102 (2016) - [j10]M. Pawan Kumar, Puneet Kumar Dokania:
Rounding-based Moves for Semi-Metric Labeling. J. Mach. Learn. Res. 17: 91:1-91:42 (2016) - [c36]James Pritts, Denys Rozumnyi, M. Pawan Kumar, Ondrej Chum:
Coplanar Repeats by Energy Minimization. BMVC 2016 - [c35]Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Continuous Relaxations for Dense CRF. ECCV (2) 2016: 818-833 - [c34]Pritish Mohapatra, Puneet Kumar Dokania, C. V. Jawahar, M. Pawan Kumar:
Partial Linearization Based Optimization for Multi-class SVM. ECCV (5) 2016: 842-857 - [i13]Pritish Mohapatra, Michal Rolínek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar:
Efficient Optimization for Rank-based Loss Functions. CoRR abs/1604.08269 (2016) - [i12]Diane Bouchacourt, M. Pawan Kumar, Sebastian Nowozin:
DISCO Nets: DISsimilarity COefficient Networks. CoRR abs/1606.02556 (2016) - [i11]Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Continuous Relaxations for Dense CRF. CoRR abs/1608.06192 (2016) - [i10]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs. CoRR abs/1611.01787 (2016) - [i9]Leonard Berrada, Andrew Zisserman, M. Pawan Kumar:
Trusting SVM for Piecewise Linear CNNs. CoRR abs/1611.02185 (2016) - [i8]Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar:
Efficient Linear Programming for Dense CRFs. CoRR abs/1611.09718 (2016) - [i7]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs - Workshop Version. CoRR abs/1612.01094 (2016) - 2015
- [j9]M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller:
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 37(7): 1373-1386 (2015) - [j8]Aseem Behl, Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar:
Optimizing Average Precision Using Weakly Supervised Data. IEEE Trans. Pattern Anal. Mach. Intell. 37(12): 2545-2557 (2015) - [c33]Puneet Kumar Dokania, M. Pawan Kumar:
Parsimonious Labeling. ICCV 2015: 1760-1768 - [c32]Diane Bouchacourt, Sebastian Nowozin, M. Pawan Kumar:
Entropy-Based Latent Structured Output Prediction. ICCV 2015: 2920-2928 - [i6]Puneet Kumar Dokania, M. Pawan Kumar:
Parsimonious Labeling. CoRR abs/1507.01208 (2015) - [i5]Pankaj Pansari, M. Pawan Kumar:
Truncated Max-of-Convex Models. CoRR abs/1512.07815 (2015) - 2014
- [c31]Aseem Behl, C. V. Jawahar, M. Pawan Kumar:
Optimizing Average Precision Using Weakly Supervised Data. CVPR 2014: 1011-1018 - [c30]Puneet Kumar Dokania, Aseem Behl, C. V. Jawahar, M. Pawan Kumar:
Learning to Rank Using High-Order Information. ECCV (4) 2014: 609-623 - [c29]M. Pawan Kumar:
Rounding-based Moves for Metric Labeling. NIPS 2014: 109-117 - [c28]Pritish Mohapatra, C. V. Jawahar, M. Pawan Kumar:
Efficient Optimization for Average Precision SVM. NIPS 2014: 2312-2320 - 2013
- [b1]M. Pawan Kumar:
Weakly Supervised Learning for Structured Output Prediction. École normale supérieure de Cachan, France, 2013 - [c27]Wojciech Zaremba, M. Pawan Kumar, Alexandre Gramfort, Matthew B. Blaschko:
Learning from M/EEG Data with Variable Brain Activation Delays. IPMI 2013: 414-425 - [c26]Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar:
Discriminative Parameter Estimation for Random Walks Segmentation. MICCAI (3) 2013: 219-226 - [i4]Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar:
Discriminative Parameter Estimation for Random Walks Segmentation: Technical Report. CoRR abs/1306.1083 (2013) - [i3]Pierre-Yves Baudin, Danny Goodman, Puneet Kumar, Noura Azzabou, Pierre G. Carlier, Nikos Paragios, M. Pawan Kumar:
Discriminative Parameter Estimation for Random Walks Segmentation. CoRR abs/1308.6721 (2013) - 2012
- [j7]Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli:
A Buffer-Sizing Algorithm for Network-on-Chips with Multiple Voltage-Frequency Islands. J. Electr. Comput. Eng. 2012: 537286:1-537286:12 (2012) - [c25]M. Pawan Kumar, Benjamin Packer, Daphne Koller:
Modeling Latent Variable Uncertainty for Loss-based Learning. ICML 2012 - [c24]Kevin Miller, M. Pawan Kumar, Benjamin Packer, Danny Goodman, Daphne Koller:
Max-Margin Min-Entropy Models. AISTATS 2012: 779-787 - [i2]M. Pawan Kumar, Daphne Koller:
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts. CoRR abs/1205.2633 (2012) - [i1]M. Pawan Kumar, Benjamin Packer, Daphne Koller:
Modeling Latent Variable Uncertainty for Loss-based Learning. CoRR abs/1206.4636 (2012) - 2011
- [j6]M. Pawan Kumar, Olga Veksler, Philip H. S. Torr:
Improved Moves for Truncated Convex Models. J. Mach. Learn. Res. 12: 31-67 (2011) - [c23]M. Pawan Kumar, Haithem Turki, Dan Preston, Daphne Koller:
Learning specific-class segmentation from diverse data. ICCV 2011: 1800-1807 - [c22]M. Pawan Kumar, Anish S. Kumar, Srinivasan Murali, Luca Benini, Kamakoti Veezhinathan:
A Method for Integrating Network-on-Chip Topologies with 3D ICs. ISVLSI 2011: 60-65 - [c21]Anish S. Kumar, M. Pawan Kumar, Srinivasan Murali, V. Kamakoti, Luca Benini, Giovanni De Micheli:
A Simulation Based Buffer Sizing Algorithm for Network on Chips. ISVLSI 2011: 206-211 - 2010
- [j5]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues. IEEE Trans. Pattern Anal. Mach. Intell. 32(3): 530-545 (2010) - [c20]Pushmeet Kohli, M. Pawan Kumar:
Energy minimization for linear envelope MRFs. CVPR 2010: 1863-1870 - [c19]M. Pawan Kumar, Daphne Koller:
Efficiently selecting regions for scene understanding. CVPR 2010: 3217-3224 - [c18]M. Pawan Kumar, Benjamin Packer, Daphne Koller:
Self-Paced Learning for Latent Variable Models. NIPS 2010: 1189-1197
2000 – 2009
- 2009
- [j4]M. Pawan Kumar, Vladimir Kolmogorov, Philip H. S. Torr:
An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs. J. Mach. Learn. Res. 10: 71-106 (2009) - [j3]Pushmeet Kohli, M. Pawan Kumar, Philip H. S. Torr:
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions. IEEE Trans. Pattern Anal. Mach. Intell. 31(9): 1645-1656 (2009) - [c17]M. Pawan Kumar, Andrew Zisserman, Philip H. S. Torr:
Efficient discriminative learning of parts-based models. ICCV 2009: 552-559 - [c16]M. Pawan Kumar, Daphne Koller:
Learning a Small Mixture of Trees. NIPS 2009: 1051-1059 - [c15]M. Pawan Kumar, Daphne Koller:
MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts. UAI 2009: 313-320 - 2008
- [j2]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
Learning Layered Motion Segmentations of Video. Int. J. Comput. Vis. 76(3): 301-319 (2008) - [c14]M. Pawan Kumar, Philip H. S. Torr:
Efficiently solving convex relaxations for MAP estimation. ICML 2008: 680-687 - [c13]M. Pawan Kumar, Philip H. S. Torr:
Improved Moves for Truncated Convex Models. NIPS 2008: 889-896 - 2007
- [c12]Pushmeet Kohli, M. Pawan Kumar, Philip H. S. Torr:
P3 & Beyond: Solving Energies with Higher Order Cliques. CVPR 2007 - [c11]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
An Invariant Large Margin Nearest Neighbour Classifier. ICCV 2007: 1-8 - 2006
- [c10]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
An Object Category Specific mrffor Segmentation. Toward Category-Level Object Recognition 2006: 596-616 - [c9]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
Solving Markov Random Fields using Second Order Cone Programming Relaxations. CVPR (1) 2006: 1045-1052 - [c8]M. Pawan Kumar, Philip H. S. Torr:
Fast Memory-Efficient Generalized Belief Propagation. ECCV (4) 2006: 451-463 - [c7]Mukta Prasad, Andrew Zisserman, Andrew W. Fitzgibbon, M. Pawan Kumar, Philip H. S. Torr:
Learning Class-Specific Edges for Object Detection and Segmentation. ICVGIP 2006: 94-105 - 2005
- [c6]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
OBJ CUT. CVPR (1) 2005: 18-25 - [c5]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
Learning Layered Motion Segmentation of Video. ICCV 2005: 33-40 - 2004
- [j1]M. Pawan Kumar, Saurabh Goyal, Sujit Kuthirummal, C. V. Jawahar, P. J. Narayanan:
Discrete contours in multiple views: approximation and recognition. Image Vis. Comput. 22(14): 1229-1239 (2004) - [c4]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
Extending Pictorial Structures for Object Recognition. BMVC 2004: 1-10 - [c3]M. Pawan Kumar, C. V. Jawahar, P. J. Narayanan:
Building blocks for autonomous navigation using contour correspondences. ICIP 2004: 1381-1384 - [c2]M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserman:
Learning Layered Pictorial Structures from Video. ICVGIP 2004: 158-164 - 2002
- [c1]M. Pawan Kumar, Saurabh Goyal, C. V. Jawahar, P. J. Narayanan:
Polygonal Approximation of Closed Curves across Multiple Views. ICVGIP 2002
Coauthor Index
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