Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
K. Alahari.
Human, Motion and Other Priors for Partially-Supervised Recognition, Communauté Université Grenoble Alpes, January 2019, Habilitation à diriger des recherches.
https://hal.inria.fr/tel-02269024
[2]
K. Shmelkov.
Approaches for incremental learning and image generation, Université Grenoble Alpes, March 2019.
https://tel.archives-ouvertes.fr/tel-02183259

Articles in International Peer-Reviewed Journals

[3]
A. Bietti, J. Mairal.
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations, in: Journal of Machine Learning Research, 2019, vol. 20, no 1, pp. 1-49, https://arxiv.org/abs/1706.03078.
https://hal.inria.fr/hal-01536004
[4]
D. Chen, L. Jacob, J. Mairal.
Biological Sequence Modeling with Convolutional Kernel Networks, in: Bioinformatics, September 2019, vol. 35, no 18, pp. 3294–3302. [ DOI : 10.1093/bioinformatics/btz094 ]
https://hal.inria.fr/hal-01632912
[5]
D. Derkach, A. Ruiz, F. M. Sukno.
Tensor Decomposition and Non-linear Manifold Modeling for 3D Head Pose Estimation, in: International Journal of Computer Vision, October 2019, vol. 127, no 10, pp. 1565-1585. [ DOI : 10.1007/s11263-019-01208-x ]
https://hal.archives-ouvertes.fr/hal-02267568
[6]
G. Durif, L. Modolo, J. E. Mold, S. Lambert-Lacroix, F. Picard.
Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis, in: Bioinformatics, October 2019, vol. 20, pp. 4011–4019, https://arxiv.org/abs/1710.11028. [ DOI : 10.1093/bioinformatics/btz177 ]
https://hal.archives-ouvertes.fr/hal-01649275
[7]
N. Dvornik, J. Mairal, C. Schmid.
On the Importance of Visual Context for Data Augmentation in Scene Understanding, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, December 2019, pp. 1-15, forthcoming. [ DOI : 10.1109/TPAMI.2019.2961896 ]
https://hal.archives-ouvertes.fr/hal-01869784
[8]
H. Lin, J. Mairal, Z. Harchaoui.
An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration, in: SIAM Journal on Optimization, May 2019, vol. 29, no 2, pp. 1408-1443, https://arxiv.org/abs/1610.00960. [ DOI : 10.1137/17M1125157 ]
https://hal.inria.fr/hal-01376079
[9]
G. Rogez, P. Weinzaepfel, C. Schmid.
LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, pp. 1-15, forthcoming. [ DOI : 10.1109/TPAMI.2019.2892985 ]
https://hal.archives-ouvertes.fr/hal-01961189
[10]
P. Tokmakov, C. Schmid, K. Alahari.
Learning to Segment Moving Objects, in: International Journal of Computer Vision, March 2019, vol. 127, no 3, pp. 282–301, https://arxiv.org/abs/1712.01127. [ DOI : 10.1007/s11263-018-1122-2 ]
https://hal.archives-ouvertes.fr/hal-01653720

International Conferences with Proceedings

[11]
A. Bietti, J. Mairal.
On the Inductive Bias of Neural Tangent Kernels, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-24, https://arxiv.org/abs/1905.12173.
https://hal.inria.fr/hal-02144221
[12]
A. Bietti, G. Mialon, D. Chen, J. Mairal.
A Kernel Perspective for Regularizing Deep Neural Networks, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, Proceedings of Machine Learning Research, June 2019, vol. 97, pp. 664-674, https://arxiv.org/abs/1810.00363.
https://hal.inria.fr/hal-01884632
[13]
M. Caron, P. Bojanowski, J. Mairal, A. Joulin.
Unsupervised Pre-Training of Image Features on Non-Curated Data, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, Proceedings of the International Conference on Computer Vision (ICCV), October 2019, pp. 1-10.
https://hal.archives-ouvertes.fr/hal-02119564
[14]
D. Chen, L. Jacob, J. Mairal.
Biological Sequence Modeling with Convolutional Kernel Networks, in: RECOMB 2019 - 23rd Annual International Conference Research in Computational Molecular Biology, Washington DC, United States, Springer, May 2019, pp. 1-2. [ DOI : 10.1007/978-3-030-17083-7 ]
https://hal.archives-ouvertes.fr/hal-02388776
[15]
D. Chen, L. Jacob, J. Mairal.
Recurrent Kernel Networks, in: NeurIPS 2019 - Thirty-third Conference Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-19, https://arxiv.org/abs/1906.03200.
https://hal.inria.fr/hal-02151135
[16]
N. Crasto, P. Weinzaepfel, K. Alahari, C. Schmid.
MARS: Motion-Augmented RGB Stream for Action Recognition, in: CVPR 2019 - IEEE Conference on Computer Vision & Pattern Recognition, Long Beach, CA, United States, IEEE, June 2019, pp. 1-10.
https://hal.inria.fr/hal-02140558
[17]
N. Dvornik, C. Schmid, J. Mairal.
Diversity with Cooperation: Ensemble Methods for Few-Shot Classification, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, pp. 1-12, https://arxiv.org/abs/1903.11341 - Added experiments with different network architectures and input image resolutions.
https://hal.archives-ouvertes.fr/hal-02080004
[18]
M. Elbayad, J. Gu, E. Grave, M. Auli.
Depth-adaptive Transformer, in: ICLR 2020 - Eighth International Conference on Learning Representations, Addis Ababa, Ethiopia, December 2019, pp. 1-14.
https://hal.inria.fr/hal-02422914
[19]
V. Gabeur, J.-S. Franco, X. Martin, C. Schmid, G. Rogez.
Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, pp. 1-10.
https://hal.inria.fr/hal-02242795
[20]
Y. Hasson, G. Varol, D. Tzionas, I. Kalevatykh, M. J. Black, I. Laptev, C. Schmid.
Learning joint reconstruction of hands and manipulated objects, in: CVPR 2019 - IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, United States, IEEE, June 2019, pp. 1-14.
https://hal.archives-ouvertes.fr/hal-02429093
[21]
R. Klokov, J. Verbeek, E. Boyer.
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image, in: BMVC 2019 - British Machine Vision Conference, Cardiff, United Kingdom, September 2019, pp. 1-15, https://arxiv.org/abs/1908.07475 - Awarded with Best Science Paper Honourable Mention Award at BMVC'19..
https://hal.inria.fr/hal-02268466
[22]
A. Kulunchakov, J. Mairal.
A Generic Acceleration Framework for Stochastic Composite Optimization, in: NeurIPS 2019 - Thirty-third Conference Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-24, https://arxiv.org/abs/1906.01164.
https://hal.inria.fr/hal-02139489
[23]
A. Kulunchakov, J. Mairal.
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, June 2019, pp. 1-24, https://arxiv.org/abs/1905.02374 - short version of preprint arXiv:1901.08788.
https://hal.inria.fr/hal-02121913
[24]
T. Lucas, K. Shmelkov, K. Alahari, C. Schmid, J. Verbeek.
Adaptive Density Estimation for Generative Models, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-24.
https://hal.archives-ouvertes.fr/hal-01886285
[25]
A. Pashevich, R. Strudel, I. Kalevatykh, I. Laptev, C. Schmid.
Learning to Augment Synthetic Images for Sim2Real Policy Transfer, in: IROS 2019 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Macao, China, November 2019, pp. 1-6, https://arxiv.org/abs/1903.07740 - 7 pages.
https://hal.archives-ouvertes.fr/hal-02273326
[26]
J. Peyre, I. Laptev, C. Schmid, J. Sivic.
Detecting unseen visual relations using analogies, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, https://arxiv.org/abs/1812.05736v3.
https://hal.archives-ouvertes.fr/hal-01975760
[27]
A. Ruiz, J. Verbeek.
Adaptative Inference Cost With Convolutional Neural Mixture Models, in: ICCV 2019 - International Conference on Computer Vision, Seoul, South Korea, October 2019, pp. 1-12.
https://hal.archives-ouvertes.fr/hal-02267564
[28]
A. Sablayrolles, M. Douze, Y. Ollivier, C. Schmid, H. Jégou.
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, June 2019, https://arxiv.org/abs/1908.11229.
https://hal.inria.fr/hal-02278902
[29]
A. Sablayrolles, M. Douze, C. Schmid, H. Jégou.
Spreading vectors for similarity search, in: ICLR 2019 - 7th International Conference on Learning Representations, New Orleans, United States, May 2019, pp. 1-13, https://arxiv.org/abs/1806.03198 - Published at ICLR 2019.
https://hal.inria.fr/hal-02278905
[30]
V. Sydorov, K. Alahari, C. Schmid.
Focused Attention for Action Recognition, in: BMVC 2019 - British Machine Vision Conference, Cardiff, United Kingdom, September 2019, pp. 1-12.
https://hal.archives-ouvertes.fr/hal-02292339
[31]
M. Vladimirova, J. Verbeek, P. Mesejo, J. Arbel.
Understanding Priors in Bayesian Neural Networks at the Unit Level, in: ICML 2019 - 36th International Conference on Machine Learning, Long Beach, United States, Proceedings of the 36th International Conference on Machine Learning, June 2019, vol. 97, pp. 6458-6467, https://arxiv.org/abs/1810.05193 - 10 pages, 5 figures, ICML'19 conference. [ DOI : 10.05193 ]
https://hal.archives-ouvertes.fr/hal-02177151

Conferences without Proceedings

[32]
A. Ruiz, O. Martinez, X. Binefa, J. Verbeek.
Learning Disentangled Representations with Reference-Based Variational Autoencoders, in: ICLR workshop on Learning from Limited Labeled Data, New Orleans, United States, May 2019, pp. 1-17.
https://hal.inria.fr/hal-01896007

Other Publications

[33]
G. Chéron, A. Osokin, I. Laptev, C. Schmid.
Modeling Spatio-Temporal Human Track Structure for Action Localization, January 2019, https://arxiv.org/abs/1806.11008 - working paper or preprint.
https://hal.inria.fr/hal-01979583
[34]
A. Iscen, G. Tolias, Y. Avrithis, O. Chum, C. Schmid.
Graph Convolutional Networks for Learning with Few Clean and many Noisy Labels, November 2019, https://arxiv.org/abs/1910.00324 - working paper or preprint. [ DOI : 10.00324 ]
https://hal.inria.fr/hal-02370212
[35]
A. Kulunchakov, J. Mairal.
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise, January 2019, https://arxiv.org/abs/1901.08788 - working paper or preprint.
https://hal.inria.fr/hal-01993531
[36]
B. Lecouat, J. Ponce, J. Mairal.
Revisiting Non Local Sparse Models for Image Restoration, December 2019, working paper or preprint.
https://hal.inria.fr/hal-02414291
[37]
X. Li, S. Wang, Y. Zhao, J. Verbeek, J. Kannala.
Hierarchical Scene Coordinate Classification and Regression for Visual Localization, November 2019, https://arxiv.org/abs/1909.06216 - working paper or preprint.
https://hal.inria.fr/hal-02384675
[38]
J. Mairal.
Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more, December 2019, working paper or preprint.
https://hal.inria.fr/hal-02417766
[39]
G. Mialon, A. D'Aspremont, J. Mairal.
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions, December 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02395624
[40]
R. Strudel, A. Pashevich, I. Kalevatykh, I. Laptev, J. Sivic, C. Schmid.
Learning to combine primitive skills: A step towards versatile robotic manipulation, August 2019, https://arxiv.org/abs/1908.00722 - 11 pages.
https://hal.archives-ouvertes.fr/hal-02274969
[41]
G. Varol, I. Laptev, C. Schmid, A. Zisserman.
Synthetic Humans for Action Recognition from Unseen Viewpoints, January 2020, https://arxiv.org/abs/1912.04070 - working paper or preprint.
https://hal.inria.fr/hal-02435731