default search action
M. Pawan Kumar
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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
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
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
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
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
Linear programming-based submodular extensions for marginal estimation. Comput. Vis. Image Underst. 189 (2019) - [j12]Thomas Joy
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12(1): 287-318 (2019) - [c49]Aditya Arun
Dissimilarity Coefficient Based Weakly Supervised Object Detection. CVPR 2019: 9432-9441 - [c48]Zhenhua Wang, Tong Liu, Qinfeng Shi
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
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
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
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
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
Optimizing Average Precision Using Weakly Supervised Data. CVPR 2014: 1011-1018 - [c30]Puneet Kumar Dokania, Aseem Behl, C. V. Jawahar
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
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
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
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
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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:21 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: