PEFLL: Personalized federated learning by learning to learn
J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024.
LibreCat: https://research-explorer.ista.ac.at/record/17411
arXiv: 2306.05515

1-Lipschitz layers compared: Memory, speed, and certifiable robustness
B. Prach, F. Brau, G. Buttazzo, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–24583.
LibreCat: https://research-explorer.ista.ac.at/record/17426

On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift
P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research in Pattern Recognition, Springer Nature, 2023, pp. 67–73.
LibreCat: https://research-explorer.ista.ac.at/record/14410
DOI: 10.1007/978-3-031-40773-4_6

1-Lipschitz neural networks are more expressive with N-activations
B. Prach, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/15039
DOI: 10.48550/ARXIV.2311.06103
arXiv: 2311.06103

CrAM: A Compression-Aware Minimizer
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , OpenReview, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Deep neural collapse is provably optimal for the deep unconstrained features model
P. Súkeník, M. Mondelli, C. Lampert, in:, 37th Annual Conference on Neural Information Processing Systems, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14921
arXiv: 2305.13165

Cross-client Label Propagation for transductive federated learning
J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/12660
DOI: 10.48550/arXiv.2210.06434
arXiv: 2210.06434

Generalization in Multi-objective machine learning
P. Súkeník, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/12662
DOI: 10.48550/arXiv.2208.13499
arXiv: 2208.13499

FLEA: Provably robust fair multisource learning from unreliable training data
E.B. Iofinova, N.H. Konstantinov, C. Lampert, Transactions on Machine Learning Research (2022).
LibreCat: https://research-explorer.ista.ac.at/record/12495
arXiv: 2106.11732

Almost-orthogonal layers for efficient general-purpose Lipschitz networks
B. Prach, C. Lampert, in:, Computer Vision – ECCV 2022, Springer Nature, 2022, pp. 350–365.
LibreCat: https://research-explorer.ista.ac.at/record/11839
DOI: 10.1007/978-3-031-19803-8_21
arXiv: 2208.03160

Lightweight conditional model extrapolation for streaming data under class-prior shift
P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134.
LibreCat: https://research-explorer.ista.ac.at/record/12161
DOI: 10.1109/icpr56361.2022.9956195
WoS: 000897707602018
arXiv: 2206.05181

Fairness-aware PAC learning from corrupted data
N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

On the impossibility of fairness-aware learning from corrupted data
N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Overcoming rare-language discrimination in multi-lingual sentiment analysis
J. Lampert, C. Lampert, in:, 2021 IEEE International Conference on Big Data, IEEE, 2022, pp. 5185–5192.
LibreCat: https://research-explorer.ista.ac.at/record/10752
DOI: 10.1109/bigdata52589.2021.9672003
WoS: 000800559505036

Does SGD implicitly optimize for smoothness?
V. Volhejn, C. Lampert, in:, 42nd German Conference on Pattern Recognition, Springer, 2021, pp. 246–259.
LibreCat: https://research-explorer.ista.ac.at/record/9210
DOI: 10.1007/978-3-030-71278-5_18

The inductive bias of ReLU networks on orthogonally separable data
M. Phuong, C. Lampert, in:, 9th International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9416

Fairness through regularization for learning to rank
N.H. Konstantinov, C. Lampert, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Zero-Shot Learning
C. Lampert, in:, K. Ikeuchi (Ed.), Computer Vision, 2nd ed., Springer, Cham, 2021, pp. 1395–1397.
LibreCat: https://research-explorer.ista.ac.at/record/14987
DOI: 10.1007/978-3-030-63416-2_874

Object-centric image generation with factored depths, locations, and appearances
T. Anciukevicius, C. Lampert, P.M. Henderson, ArXiv (n.d.).
LibreCat: https://research-explorer.ista.ac.at/record/8063
arXiv: 2004.00642

Unsupervised object-centric video generation and decomposition in 3D
P.M. Henderson, C. Lampert, in:, 34th Conference on Neural Information Processing Systems, Curran Associates, 2020, pp. 3106–3117.
LibreCat: https://research-explorer.ista.ac.at/record/8188
arXiv: 2007.06705

Localizing grouped instances for efficient detection in low-resource scenarios
A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7936
DOI: 10.1109/WACV45572.2020.9093288
arXiv: 2004.12623

A flexible selection scheme for minimum-effort transfer learning
A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7937
DOI: 10.1109/WACV45572.2020.9093635
arXiv: 2008.11995

Functional vs. parametric equivalence of ReLU networks
M. Phuong, C. Lampert, in:, 8th International Conference on Learning Representations, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7481

On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Leveraging 2D data to learn textured 3D mesh generation
P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.
LibreCat: https://research-explorer.ista.ac.at/record/8186
DOI: 10.1109/CVPR42600.2020.00752
arXiv: 2004.04180

KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications
R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995.
LibreCat: https://research-explorer.ista.ac.at/record/6944
DOI: 10.1007/s11263-019-01232-x
WoS: 000494406800001

Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt
K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7171
DOI: 10.1007/978-3-658-26763-6

Strategy representation by decision trees with linear classifiers
P. Ashok, T. Brázdil, K. Chatterjee, J. Ketínský, C. Lampert, V. Toman, in:, 16th International Conference on Quantitative Evaluation of Systems, Springer Nature, 2019, pp. 109–128.
LibreCat: https://research-explorer.ista.ac.at/record/6942
DOI: 10.1007/978-3-030-30281-8_7
WoS: 000679281300007
arXiv: 1906.08178

Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly
Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019) 2251–2265.
LibreCat: https://research-explorer.ista.ac.at/record/6554
DOI: 10.1109/tpami.2018.2857768
WoS: 000480343900015
arXiv: 1707.00600

Distillation-based training for multi-exit architectures
M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision, IEEE, 2019, pp. 1355–1364.
LibreCat: https://research-explorer.ista.ac.at/record/7479
DOI: 10.1109/ICCV.2019.00144
WoS: 000531438101047

Detecting visual relationships using box attention
A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7640
DOI: 10.1109/ICCVW.2019.00217
WoS: 000554591601098
arXiv: 1807.02136

Towards understanding knowledge distillation
M. Phuong, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 5142–5151.
LibreCat: https://research-explorer.ista.ac.at/record/6569

Robust learning from untrusted sources
N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.
LibreCat: https://research-explorer.ista.ac.at/record/6590
arXiv: 1901.10310

KS(conf): A light-weight test if a ConvNet operates outside of Its specifications
R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.
LibreCat: https://research-explorer.ista.ac.at/record/6482
DOI: 10.1007/978-3-030-12939-2_18
arXiv: 1804.04171

Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis
T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1029–1031.
LibreCat: https://research-explorer.ista.ac.at/record/321
DOI: 10.1109/TPAMI.2018.2804998
WoS: 000428901200001

Learning intelligent dialogs for bounding box annotation
J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.
LibreCat: https://research-explorer.ista.ac.at/record/10882
DOI: 10.1109/cvpr.2018.00956
WoS: 000457843609036
arXiv: 1712.08087

Learning equations for extrapolation and control
S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

Data-dependent stability of stochastic gradient descent
I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference on Machine Learning, ML Research Press, 2018, pp. 2815–2824.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

Extrapolation and learning equations
G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/6841
arXiv: 1610.02995

Optimal geospatial volunteer allocation needs realistic distances
J. Pielorz, M. Prandtstetter, M. Straub, C. Lampert, in:, 2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–3763.
LibreCat: https://research-explorer.ista.ac.at/record/750
DOI: 10.1109/BigData.2017.8258375

PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914.
LibreCat: https://research-explorer.ista.ac.at/record/1000
WoS: 000683309501102
arXiv: 1612.08185

iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
LibreCat: https://research-explorer.ista.ac.at/record/998
DOI: 10.1109/CVPR.2017.587
WoS: 000418371405066

Probabilistic image colorization
A. Royer, A. Kolesnikov, C. Lampert, in:, BMVA Press, 2017, p. 85.1-85.12.
LibreCat: https://research-explorer.ista.ac.at/record/911
DOI: 10.5244/c.31.85
arXiv: 1705.04258

Learning theory for conditional risk minimization
A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.
LibreCat: https://research-explorer.ista.ac.at/record/1108
WoS: 000509368500024

Multi-task learning with labeled and unlabeled tasks
A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.
LibreCat: https://research-explorer.ista.ac.at/record/999
WoS: 000683309502093

Improving weakly-supervised object localization by micro-annotation
A. Kolesnikov, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2016, BMVA Press, 2016, p. 92.1-92.12.
LibreCat: https://research-explorer.ista.ac.at/record/1102
DOI: 10.5244/C.30.92

Seed, expand and constrain: Three principles for weakly-supervised image segmentation
A. Kolesnikov, C. Lampert, in:, Springer, 2016, pp. 695–711.
LibreCat: https://research-explorer.ista.ac.at/record/1369
DOI: 10.1007/978-3-319-46493-0_42

Optimal geospatial allocation of volunteers for crisis management
J. Pielorz, C. Lampert, in:, IEEE, 2016.
LibreCat: https://research-explorer.ista.ac.at/record/1707
DOI: 10.1109/ICT-DM.2015.7402041

Lifelong learning with non-i.i.d. tasks
A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015, pp. 1540–1548.
LibreCat: https://research-explorer.ista.ac.at/record/1425

A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle
N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Classifier adaptation at prediction time
A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

Predicting the future behavior of a time-varying probability distribution
C. Lampert, in:, IEEE, 2015, pp. 942–950.
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

Movement templates for learning of hitting and batting
K. Muelling, O. Kroemer, C. Lampert, B. Schölkopf, in:, J. Kober, J. Peters (Eds.), Learning Motor Skills, Springer, 2014, pp. 69–82.
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 10.1007/978-3-319-03194-1_3

Mind the nuisance: Gaussian process classification using privileged noise
D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, N. Quadrianto, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems, 2014, pp. 837–845.
LibreCat: https://research-explorer.ista.ac.at/record/2033

Closed-form approximate CRF training for scalable image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

CoConut: Co-classification with output space regularization
S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2173

Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters
V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–1409.
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182

A PAC-Bayesian bound for Lifelong Learning
A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.
LibreCat: https://research-explorer.ista.ac.at/record/2160

Drosophila Embryo Stage Annotation using Label Propagation
T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013.
LibreCat: https://research-explorer.ista.ac.at/record/2294
DOI: 10.1109/ICCV.2013.139

Learning to rank using privileged information
V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.
LibreCat: https://research-explorer.ista.ac.at/record/2293
DOI: 10.1109/ICCV.2013.107

Attribute-based classification for zero-shot learning of object categories
C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis and Machine Intelligence 36 (2013) 453–465.
LibreCat: https://research-explorer.ista.ac.at/record/2516
DOI: 10.1109/TPAMI.2013.140

Computing the M most probable modes of a graphical model
C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013, pp. 161–169.
LibreCat: https://research-explorer.ista.ac.at/record/2901

Beyond dataset bias: Multi-task unaligned shared knowledge transfer
T. Tommasi, N. Quadrianto, B. Caputo, C. Lampert, 7724 (2013) 1–15.
LibreCat: https://research-explorer.ista.ac.at/record/2948
DOI: 10.1007/978-3-642-37331-2_1

Kernel based learning
N. Quadrianto, C. Lampert, in:, W. Dubitzky, O. Wolkenhauer, K. Cho, H. Yokota (Eds.), Encyclopedia of Systems Biology, Springer, 2013, pp. 1069–1069.
LibreCat: https://research-explorer.ista.ac.at/record/3321
DOI: 10.1007/978-1-4419-9863-7_604

Dynamic pruning of factor graphs for maximum marginal prediction
C. Lampert, in:, Neural Information Processing Systems, 2012, pp. 82–90.
LibreCat: https://research-explorer.ista.ac.at/record/2825

Guest editorial: Special issue on structured prediction and inference
M. Blaschko, C. Lampert, International Journal of Computer Vision 99 (2012) 257–258.
LibreCat: https://research-explorer.ista.ac.at/record/3164
DOI: 10.1007/s11263-012-0530-y

Augmented attribute representations
V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255.
LibreCat: https://research-explorer.ista.ac.at/record/3125
DOI: 10.1007/978-3-642-33715-4_18

Information theoretic clustering using minimal spanning trees
A. Müller, S. Nowozin, C. Lampert, in:, Springer, 2012, pp. 205–215.
LibreCat: https://research-explorer.ista.ac.at/record/3126
DOI: 10.1007/978-3-642-32717-9_21

Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components
C. Lampert, J. Peters, Journal of Real-Time Image Processing 7 (2012) 31–41.
LibreCat: https://research-explorer.ista.ac.at/record/3248
DOI: 10.1007/s11554-010-0168-3

Approximating marginals using discrete energy minimization
F. Korc, V. Kolmogorov, C. Lampert, in:, ICML, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/3124

Approximating marginals using discrete energy minimization
F. Korc, V. Kolmogorov, C. Lampert, Approximating Marginals Using Discrete Energy Minimization, IST Austria, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/5396
DOI: 10.15479/AT:IST-2012-0003

Multi-modal learning for dynamic tactile sensing
O. Kroemer, C. Lampert, J. Peters, in:, Deutsches Zentrum für Luft und Raumfahrt, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/2915

The most persistent soft-clique in a set of sampled graphs
N. Quadrianto, C. Lampert, C. Chen, in:, Proceedings of the 29th International Conference on Machine Learning, ML Research Press, 2012, pp. 211–218.
LibreCat: https://research-explorer.ista.ac.at/record/3127

Learning anticipation policies for robot table tennis
Z. Wang, C. Lampert, K. Mülling, B. Schölkopf, J. Peters, in:, IEEE, 2011, pp. 332–337.
LibreCat: https://research-explorer.ista.ac.at/record/3337
DOI: 10.1109/IROS.2011.6094892

Semi supervised kernel canonical correlation analysis with application to human fMRI
M. Blaschko, J. Shelton, A. Bartels, C. Lampert, A. Gretton, Pattern Recognition Letters 32 (2011) 1572–1583.
LibreCat: https://research-explorer.ista.ac.at/record/3389
DOI: 10.1016/j.patrec.2011.02.011

Learning dynamic tactile sensing with robust vision based training
O. Kroemer, C. Lampert, J. Peters, IEEE Transactions on Robotics 27 (2011) 545–557.
LibreCat: https://research-explorer.ista.ac.at/record/3382
DOI: 10.1109/TRO.2011.2121130

Enforcing topological constraints in random field image segmentation
C. Chen, D. Freedman, C. Lampert, Enforcing Topological Constraints in Random Field Image Segmentation, IST Austria, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/5386
DOI: 10.15479/AT:IST-2011-0002

Enforcing topological constraints in random field image segmentation
C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–2096.
LibreCat: https://research-explorer.ista.ac.at/record/3336
DOI: 10.1109/CVPR.2011.5995503

Maximum margin multi-label structured prediction
C. Lampert, in:, Neural Information Processing Systems, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3163

Maximum margin multi label structured prediction
C. Lampert, Maximum Margin Multi Label Structured Prediction, Neural Information Processing Systems Foundation, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3322

Structured learning and prediction in computer vision
S. Nowozin, C. Lampert, Foundations and Trends in Computer Graphics and Vision 6 (2011) 185–365.
LibreCat: https://research-explorer.ista.ac.at/record/3320
DOI: 10.1561/0600000033

Learning multi-view neighborhood preserving projections
N. Quadrianto, C. Lampert, in:, ML Research Press, 2011, pp. 425–432.
LibreCat: https://research-explorer.ista.ac.at/record/3319

Global interactions in random field models: A potential function ensuring connectedness
S. Nowozin, C. Lampert, SIAM Journal on Imaging Sciences 3 (2010) 1048–1074.
LibreCat: https://research-explorer.ista.ac.at/record/3686
DOI: 10.1137/090752614

Optimizing one-shot recognition with micro-set learning
K. Tang, M. Tappen, R. Sukthankar, C. Lampert, in:, IEEE, 2010, pp. 3027–3034.
LibreCat: https://research-explorer.ista.ac.at/record/3682
DOI: 10.1109/CVPR.2010.5540053

Movement templates for learning of hitting and batting
J. Kober, K. Mülling, O. Krömer, C. Lampert, B. Schölkopf, J. Peters, in:, IEEE, 2010, pp. 853–858.
LibreCat: https://research-explorer.ista.ac.at/record/3702
DOI: 10.1109/ROBOT.2010.5509672

Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning
C. Lampert, O. Krömer, in:, Springer, 2010, pp. 566–579.
LibreCat: https://research-explorer.ista.ac.at/record/3794
DOI: 10.1007/978-3-642-15552-9_41

Topic models for semantic video compression
J. Wanke, A. Ulges, C. Lampert, T. Breuel, in:, ACM, 2010, pp. 275–284.
LibreCat: https://research-explorer.ista.ac.at/record/3676
DOI: 10.1145/1743384.1743433

Unsupervised object discovery: A comparison
T. Tuytelaars, C. Lampert, M. Blaschko, W. Buntine, International Journal of Computer Vision 88 (2010) 284–302.
LibreCat: https://research-explorer.ista.ac.at/record/3697
DOI: 10.1007/s11263-009-0271-8

An efficient divide-and-conquer cascade for nonlinear object detection
C. Lampert, in:, IEEE, 2010, pp. 1022–1029.
LibreCat: https://research-explorer.ista.ac.at/record/3713
DOI: 10.1109/CVPR.2010.5540107

On parameter learning in CRF-based approaches to object class image segmentation
S. Nowozin, P. Gehler, C. Lampert, in:, Springer, 2010, pp. 98–111.
LibreCat: https://research-explorer.ista.ac.at/record/3793
DOI: 10.1007/978-3-642-15567-3_8

Semi-supervised analysis of human fMRI data
M. Blaschko, C. Lampert, A. Bartels, Semi-Supervised Analysis of Human FMRI Data, Berlin Institute of Technology, 2009.
LibreCat: https://research-explorer.ista.ac.at/record/3699

Object localization with global and local context kernels
M. Blaschko, C. Lampert, in:, BMVA Press, 2009, pp. 1–11.
LibreCat: https://research-explorer.ista.ac.at/record/3703
DOI: 10.5244/C.23.63

Learning to detect unseen object classes by between-class attribute transfer
C. Lampert, H. Nickisch, S. Harmeling, in:, IEEE, 2009, pp. 951–958.
LibreCat: https://research-explorer.ista.ac.at/record/3704
DOI: 10.1109/CVPR.2009.5206594

Active structured learning for high-speed object detection
C. Lampert, J. Peters, in:, Springer, 2009, pp. 221–231.
LibreCat: https://research-explorer.ista.ac.at/record/3715
DOI: 10.1007/978-3-642-03798-6_23

A high-speed object tracker from off-the-shelf components
C. Lampert, J. Peters, A High-Speed Object Tracker from off-the-Shelf Components, IEEE, 2009.
LibreCat: https://research-explorer.ista.ac.at/record/3717

Structured prediction by joint kernel support estimation
C. Lampert, M. Blaschko, Machine Learning 77 (2009) 249–269.
LibreCat: https://research-explorer.ista.ac.at/record/3696
DOI: 10.1007/s10994-009-5111-0

Combining appearance and motion for human action classification in videos
P. Dhillon, S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 22–29.
LibreCat: https://research-explorer.ista.ac.at/record/3690
DOI: 10.1109/CVPRW.2009.5204237

Efficient subwindow search: A branch and bound framework for object localization
C. Lampert, M. Blaschko, T. Hofmann, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2009) 2129–2142.
LibreCat: https://research-explorer.ista.ac.at/record/3710
DOI: 10.1109/TPAMI.2009.144

Combining appearance and motion for human action classification in videos
P. Dhillon, S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 22–29.
LibreCat: https://research-explorer.ista.ac.at/record/3711
DOI: 10.1109/CVPRW.2009.5204237