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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 4N. Chesneau, K. Alahari, C. Schmid.

    Learning from Web Videos for Event Classification, in: IEEE Transactions on Circuits and Systems for Video Technology, 2018. [ DOI : 10.1109/TCSVT.2017.2764624 ]

    https://hal.inria.fr/hal-01618400
  • 5T. Dias-Alves, J. Mairal, M. Blum.

    Loter: A software package to infer local ancestry for a wide range of species, in: Molecular Biology and Evolution, June 2018, vol. 35, no 9, pp. 2318 - 2326. [ DOI : 10.1093/molbev/msy126 ]

    https://hal.inria.fr/hal-01630228
  • 6G. Durif, L. Modolo, J. Michaelsson, J. E. Mold, S. Lambert-Lacroix, F. Picard.

    High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression, in: Bioinformatics, February 2018, vol. 34, no 3, pp. 485-493, https://arxiv.org/abs/1502.05933. [ DOI : 10.1093/bioinformatics/btx571 ]

    https://hal.archives-ouvertes.fr/hal-01587360
  • 7B. Ham, M. Cho, C. Schmid, J. Ponce.

    Proposal Flow: Semantic Correspondences from Object Proposals, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, vol. 40, no 7, pp. 1711-1725. [ DOI : 10.1109/TPAMI.2017.2724510 ]

    https://hal.inria.fr/hal-01644132
  • 8G. Hu, X. Peng, Y. Yang, T. Hospedales, J. Verbeek.

    Frankenstein: Learning Deep Face Representations using Small Data, in: IEEE Transactions on Image Processing, January 2018, vol. 27, no 1, pp. 293-303, https://arxiv.org/abs/1603.06470. [ DOI : 10.1109/TIP.2017.2756450 ]

    https://hal.inria.fr/hal-01306168
  • 9H. Lin, J. Mairal, Z. Harchaoui.

    Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice, in: Journal of Machine Learning Research, April 2018, vol. 18, no 212, pp. 1-54, http://jmlr.org/papers/volume18/17-748/17-748.pdf.

    https://hal.inria.fr/hal-01664934
  • 10A. Mensch, J. Mairal, B. Thirion, G. Varoquaux.

    Stochastic Subsampling for Factorizing Huge Matrices, in: IEEE Transactions on Signal Processing, January 2018, vol. 66, no 1, pp. 113-128, https://arxiv.org/abs/1701.05363. [ DOI : 10.1109/TSP.2017.2752697 ]

    https://hal.archives-ouvertes.fr/hal-01431618
  • 11G. Rogez, C. Schmid.

    Image-based Synthesis for Deep 3D Human Pose Estimation, in: International Journal of Computer Vision, September 2018, vol. 126, no 9, pp. 993–1008, https://arxiv.org/abs/1802.04216. [ DOI : 10.1007/s11263-018-1071-9 ]

    https://hal.inria.fr/hal-01717188
  • 12G. 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.

    https://hal.archives-ouvertes.fr/hal-01961189
  • 13J. S. Supancic, G. Rogez, Y. Yang, J. Shotton, D. Ramanan.

    Depth-based hand pose estimation: methods, data, and challenges, in: International Journal of Computer Vision, November 2018, vol. 126, no 11, pp. 1180–1198. [ DOI : 10.1007/s11263-018-1081-7 ]

    https://hal.inria.fr/hal-01759416
  • 14P. Tokmakov, C. Schmid, K. Alahari.

    Learning to Segment Moving Objects, in: International Journal of Computer Vision, 2018, https://arxiv.org/abs/1712.01127.

    https://hal.archives-ouvertes.fr/hal-01653720
  • 15G. Varol, I. Laptev, C. Schmid.

    Long-term Temporal Convolutions for Action Recognition, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, June 2018, vol. 40, no 6, pp. 1510-1517, https://arxiv.org/abs/1604.04494. [ DOI : 10.1109/TPAMI.2017.2712608 ]

    https://hal.inria.fr/hal-01241518
  • 16V. Zadrija, J. Krapac, S. Šegvić, J. Verbeek.

    Sparse weakly supervised models for object localization in road environment, in: Computer Vision and Image Understanding, 2018, pp. 1-13. [ DOI : 10.1016/j.cviu.2018.10.004 ]

    https://hal.inria.fr/hal-01900418

International Conferences with Proceedings

  • 17F. M. Castro, M. J. Marín-Jiménez, N. Guil, C. Schmid, K. Alahari.

    End-to-End Incremental Learning, in: ECCV 2018 - European Conference on Computer Vision, Munich, Germany, V. Ferrari, M. Hebert, C. Sminchisescu, Y. Weiss (editors), Lecture Notes in Computer Science, September 2018, https://arxiv.org/abs/1807.09536.

    https://hal.inria.fr/hal-01849366
  • 18V. Choutas, P. Weinzaepfel, J. Revaud, C. Schmid.

    PoTion: Pose MoTion Representation for Action Recognition, in: CVPR 2018 - IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, United States, IEEE, June 2018, pp. 1-10.

    https://hal.inria.fr/hal-01764222
  • 19G. Chéron, J.-B. Alayrac, I. Laptev, C. Schmid.

    A flexible model for training action localization with varying levels of supervision, in: NIPS 2018 - 32nd Conference on Neural Information Processing Systems, Montréal, Canada, December 2018, pp. 1-17, https://arxiv.org/abs/1806.11328.

    https://hal.inria.fr/hal-01937002
  • 20C. Couprie, P. Luc, J. Verbeek.

    Joint Future Semantic and Instance Segmentation Prediction, in: ECCV Workshop on Anticipating Human Behavior, Munich, Germany, 2018.

    https://hal.inria.fr/hal-01867746
  • 21M. Douze, A. Sablayrolles, H. Jégou.

    Link and code: Fast indexing with graphs and compact regression codes, in: CVPR 2018 - IEEE Conference on Computer Vision & Pattern Recognition, Salt Lake City, United States, IEEE, June 2018, pp. 1-9.

    https://hal.inria.fr/hal-01955971
  • 22G. Durif, L. Modolo, J. E. Mold, S. Lambert-Lacroix, F. Picard.

    Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis, in: RECOMB 2018 - 22nd Annual International Conference on Research in Computational Molecular Biology, Paris, France, B. J. Raphael (editor), Lecture Notes in Bioinformatics, Springer, April 2018, vol. 10812, pp. 254-255.

    https://hal.archives-ouvertes.fr/hal-01962030
  • 23N. Dvornik, J. Mairal, C. Schmid.

    Modeling Visual Context is Key to Augmenting Object Detection Datasets, in: ECCV 2018 - European Conference on Computer Vision, Munich, Germany, LNCS, Springer, September 2018, vol. 11216, pp. 375-391, https://arxiv.org/abs/1807.07428. [ DOI : 10.1007/978-3-030-01258-8_23 ]

    https://hal.archives-ouvertes.fr/hal-01844474
  • 24M. Elbayad, L. Besacier, J. Verbeek.

    Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction, in: CoNLL 2018 - Conference on Computational Natural Language Learning, Brussels, Belgium, October 2018, pp. 1-11.

    https://hal.inria.fr/hal-01851612
  • 25M. Elbayad, L. Besacier, J. Verbeek.

    Token-level and sequence-level loss smoothing for RNN language models, in: ACL - 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018.

    https://hal.inria.fr/hal-01790879
  • 26C. Gu, C. Sun, D. Ross, C. Vondrick, C. Pantofaru, Y. Li, S. Vijayanarasimhan, G. Toderici, S. Ricco, R. R. Sukthankar, C. Schmid, J. Malik.

    AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions, in: CVPR 2018 - Computer Vision and Pattern Recognition, Salt Lake City, United States, IEEE, June 2018, pp. 1-10.

    https://hal.inria.fr/hal-01764300
  • 27X. Li, J. Ylioinas, J. Verbeek, J. Kannala.

    Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization, in: ECCV 2018 - Workshop Geometry Meets Deep Learning, Munich, Germany, September 2018, pp. 1-16.

    https://hal.inria.fr/hal-01867143
  • 28P. Luc, C. Couprie, Y. Lecun, J. Verbeek.

    Predicting Future Instance Segmentation by Forecasting Convolutional Features, in: ECCV 2018 - European Conference on Computer Vision, Munich, Germany, September 2018, pp. 1-21.

    https://hal.inria.fr/hal-01757669
  • 29T. Lucas, C. Tallec, J. Verbeek, Y. Ollivier.

    Mixed batches and symmetric discriminators for GAN training, in: ICML - 35th International Conference on Machine Learning, Stockholm, Sweden, July 2018.

    https://hal.inria.fr/hal-01791126
  • 30T. Lucas, J. Verbeek.

    Auxiliary Guided Autoregressive Variational Autoencoders, in: ECML-PKDD 2018 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Dublin, Ireland, September 2018, pp. 1-16.

    https://hal.inria.fr/hal-01652881
  • 31C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, Z. Harchaoui.

    Catalyst for Gradient-based Nonconvex Optimization, in: AISTATS 2018 - 21st International Conference on Artificial Intelligence and Statistics, Lanzarote, Spain, April 2018, pp. 1-10.

    https://hal.inria.fr/hal-01773296
  • 32K. Shmelkov, C. Schmid, K. Alahari.

    How good is my GAN?, in: ECCV 2018 - European Conference on Computer Vision, Munich, Germany, V. Ferrari, M. Hebert, C. Sminchisescu, Y. Weiss (editors), Lecture Notes in Computer Science, September 2018, pp. 1-20, https://arxiv.org/abs/1807.09499.

    https://hal.inria.fr/hal-01850447
  • 33G. A. Sigurdsson, A. Gupta, C. Schmid, A. Farhadi, K. Alahari.

    Actor and Observer: Joint Modeling of First and Third-Person Videos, in: CVPR 2018 - IEEE Conference on Computer Vision & Pattern Recognition, Salt Lake City, Utah, United States, IEEE, June 2018, pp. 1-6, https://arxiv.org/abs/1804.09627.

    https://hal.inria.fr/hal-01755547
  • 34G. Varol, D. Ceylan, B. Russell, J. Yang, E. Yumer, I. Laptev, C. Schmid.

    BodyNet: Volumetric Inference of 3D Human Body Shapes, in: ECCV 2018 - 15th European Conference on Computer Vision, Munich, Germany, September 2018, pp. 1-27, https://arxiv.org/abs/1804.04875.

    https://hal.inria.fr/hal-01852169
  • 35N. Verma, E. Boyer, J. Verbeek.

    FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis, in: CVPR - IEEE Conference on Computer Vision & Pattern Recognition, Salt Lake City, United States, June 2018, pp. 1-9, https://arxiv.org/abs/1706.05206.

    https://hal.inria.fr/hal-01540389
  • 36D. Wynen, C. Schmid, J. Mairal.

    Unsupervised Learning of Artistic Styles with Archetypal Style Analysis, in: NeurIPS 2018, Montréal, Canada, December 2018, Accepted at NIPS 2018, Montréal, Canada.

    https://hal.inria.fr/hal-01802131

Other Publications



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