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
XML PDF e-pub
PDF e-Pub


Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
C. Maigrot.
Detection of misleading information on social networks, Université de Rennes 1 [UR1], April 2019.
https://tel.archives-ouvertes.fr/tel-02404234

Articles in International Peer-Reviewed Journals

[2]
L. Amsaleg, M. E. Houle, E. Schubert.
Introduction to Special Issue of the 9th International Conference on Similarity Search and Applications (SISAP 2016), in: Information Systems, February 2019, vol. 80, 107 p. [ DOI : 10.1016/j.is.2018.11.006 ]
https://hal.inria.fr/hal-02106914
[3]
M. Cui, L. Li, M. Shi.
A Selective Biogeography-Based Optimizer Considering Resource Allocation for Large-Scale Global Optimization, in: Computational Intelligence and Neuroscience, July 2019, pp. 1-18. [ DOI : 10.1155/2019/1240162 ]
https://hal.inria.fr/hal-02383076
[4]
C. Dalloux, N. Grabar, V. Claveau.
Detecting negation: machine learning and a corpus for French, in: Revue des Sciences et Technologies de l'Information - Série TSI : Technique et Science Informatiques, December 2019, pp. 1-21.
https://hal.archives-ouvertes.fr/hal-02402913
[5]
S. Li, X. Song, H. Lu, L. Zeng, M. Shi, F. Liu.
Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm, in: Expert Systems with Applications, July 2019, vol. 139, pp. 1-11. [ DOI : 10.1016/j.eswa.2019.112839 ]
https://hal.inria.fr/hal-02383107
[6]
N. Papanelopoulos, Y. Avrithis, S. Kollias.
Revisiting the medial axis for planar shape decomposition, in: Computer Vision and Image Understanding, February 2019, vol. 179, pp. 66-78. [ DOI : 10.1016/j.cviu.2018.10.007 ]
https://hal.inria.fr/hal-01930939
[7]
O. Siméoni, A. Iscen, G. Tolias, Y. Avrithis, O. Chum.
Graph-based Particular Object Discovery, in: Machine Vision and Applications, March 2019, vol. 30, no 2, pp. 243-254. [ DOI : 10.1007/s00138-019-01005-z ]
https://hal.inria.fr/hal-02370238

International Conferences with Proceedings

[8]
A. Antonini, F. Benatti, E. King, F. Vignale, G. Gravier.
Modelling changes in diaries, correspondence and authors' libraries to support research on reading: the READ-IT approach, in: ODOCH 2019 - First International Workshop on Open Data and Ontologies for Cultural Heritage, Rome, Italy, June 2019, pp. 1-12.
https://hal.archives-ouvertes.fr/hal-02130008
[9]
C. B. El Vaigh, F. Goasdoué, G. Gravier, P. Sébillot.
Using Knowledge Base Semantics in Context-Aware Entity Linking, in: DocEng 2019 - 19th ACM Symposium on Document Engineering, Berlin, Germany, ACM, September 2019, pp. 1-10. [ DOI : 10.1007/978-3-030-27520-4_8 ]
https://hal.inria.fr/hal-02171981
[10]
M. Gheisari, T. Furon, L. Amsaleg.
Privacy Preserving Group Membership Verification and Identification, in: CVPR 2019 - IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, United States, IEEE, June 2019, pp. 1-9, https://arxiv.org/abs/1904.10327.
https://hal.archives-ouvertes.fr/hal-02107442
[11]
S. Gíslason, B. Þ. Jónsson, L. Amsaleg.
Integration of Exploration and Search: A Case Study of the M3 Model, in: MMM 2019 - 25th International Conference on MultiMedia Modeling, Thessaloniki, Greece, LNCS, Springer, December 2019, vol. 11295, pp. 156-168. [ DOI : 10.1007/978-3-030-05710-7_13 ]
https://hal.inria.fr/hal-02106893
[12]
A. Iscen, G. Tolias, Y. Avrithis, O. Chum.
Label Propagation for Deep Semi-supervised Learning, in: CVPR 2019 - IEEE Computer Vision and Pattern Recognition Conference, Long Beach, United States, IEEE, June 2019, pp. 1-10.
https://hal.inria.fr/hal-02370297
[13]
C. Leverger, S. Malinowski, T. Guyet, V. Lemaire, A. Bondu, A. Termier.
Toward a Framework for Seasonal Time Series Forecasting Using Clustering, in: IDEAL 2019, Manchester, United Kingdom, October 2019, pp. 328-340. [ DOI : 10.1007/978-3-030-33607-3_36 ]
https://hal.inria.fr/hal-02371221
[14]
Y. Lifchitz, Y. Avrithis, S. Picard, A. Bursuc.
Dense Classification and Implanting for Few-Shot Learning, in: CVPR 2019 - IEEE Computer Vision and Pattern Recognition Conference, Long Beach, United States, IEEE, June 2019, pp. 1-10.
https://hal.inria.fr/hal-02371279
[15]
Y. Liu, M. Shi, Q. Zhao, X. Wang.
Point in, Box out: Beyond Counting Persons in Crowds, in: CVPR 2019 - IEEE Computer Vision and Pattern Recognition Conference, Long Beach, United States, IEEE, June 2019, pp. 1-10.
https://hal.inria.fr/hal-02383057
[16]
H. Ragnarsdóttir, Þ. Þorleiksdóttir, O. S. Khan, B. Þ. Jónsson, G. Þ. Guðmundsson, J. Zahálka, S. Rudinac, L. Amsaleg, M. Worring.
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images, in: MM 2019 - 27th ACM International Conference on Multimedia, Nice, France, ACM, October 2019, pp. 1029-1031. [ DOI : 10.1145/3343031.3350580 ]
https://hal.inria.fr/hal-02378272
[17]
F. C. G. Reis, R. Almeida, E. Kijak, S. Malinowski, S. J. F. Guimarães, Z. do Patrocinio.
Combining convolutional side-outputs for road image segmentation, in: IJCNN 2019 - International Joint Conference on Neural Networks, Budapest, Hungary, IEEE, July 2019, pp. 1-8. [ DOI : 10.1109/IJCNN.2019.8851843 ]
https://hal.inria.fr/hal-02370834
[18]
M. Shi, Z. Yang, C. Xu, Q. Chen.
Revisiting Perspective Information for Efficient Crowd Counting, in: CVPR 2019 - IEEE Computer Vision and Pattern Recognition Conference, Long Beach, United States, IEEE, June 2019, pp. 1-10.
https://hal.inria.fr/hal-01831109
[19]
O. Siméoni, Y. Avrithis, O. Chum.
Local Features and Visual Words Emerge in Activations, in: CVPR 2019 - IEEE Computer Vision and Pattern Recognition Conference, Long Beach, United States, IEEE, June 2019, pp. 1-10.
https://hal.archives-ouvertes.fr/hal-02374156
[20]
R. Souza, R. Almeida, R. Miranda, Z. Kleber Gonçalves do Patrocinio, S. Malinowski, S. J. F. Guimarães.
BRIEF-based mid-level representations for time series classification, in: CIARP 2019 - 24th Iberoamerican Congress on Pattern Recognition, La Havane, Cuba, October 2019, pp. 449-457. [ DOI : 10.1007/978-3-030-33904-3_42 ]
https://hal.inria.fr/hal-02371260

Conferences without Proceedings

[21]
C. Dalloux, V. Claveau, N. Grabar.
Speculation and negation detection in french biomedical corpora, in: RANLP 2019 - Recent Advances in Natural Language Processing, Varna, Bulgaria, September 2019, pp. 1-10.
https://hal.archives-ouvertes.fr/hal-02284444
[22]
T. Furon.
Watermarking error exponents in the presence of noise: The case of the dual hypercone detector, in: IH&MMSEC'19 - 7th ACM Workshop on Information Hiding and Multimedia Security, Paris, France, ACM, July 2019, pp. 173-181. [ DOI : 10.1145/3335203.3335731 ]
https://hal.archives-ouvertes.fr/hal-02122206
[23]
M. Gheisari, T. Furon, L. Amsaleg.
Group Membership Verification with Privacy: Sparse or Dense?, in: WIFS 2019 - IEEE International Workshop on Information Forensics and Security, Delft, Netherlands, IEEE, December 2019, pp. 1-6.
https://hal.archives-ouvertes.fr/hal-02307926
[24]
M. Gheisari, T. Furon, L. Amsaleg, B. Razeghi, S. Voloshynovskiy.
Aggregation and embedding for group membership verification, in: ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, IEEE, May 2019, pp. 2592-2596, https://arxiv.org/abs/1812.03943 - accepted at ICASSP 2019. [ DOI : 10.1109/ICASSP.2019.8682422 ]
https://hal.archives-ouvertes.fr/hal-02091036
[25]
N. Grabar, C. Grouin, T. Hamon, V. Claveau.
Annotated corpus with clinical cases in French, in: TALN 2019 - 26e Conference on Traitement Automatique des Langues Naturelles, Toulouse, France, July 2019, pp. 1-14.
https://hal.archives-ouvertes.fr/hal-02391878
[26]
N. Grabar, C. Grouin, T. Hamon, V. Claveau.
Information Retrieval and Information Extraction from Clinical Cases. Presentation of the DEFT 2019 Challenge, in: DEFT 2019 - Défi fouille de texte, Toulouse, France, July 2019, pp. 1-10.
https://hal.archives-ouvertes.fr/hal-02280852
[27]
C. Grouin, N. Grabar, V. Claveau, T. Hamon.
Clinical Case Reports for NLP, in: BioNLP 2019 - 18th ACL Workshop on Biomedical Natural Language Processing, Florence, Italy, ACL, August 2019, pp. 273–282. [ DOI : 10.18653/v1/W19-5029 ]
https://hal.archives-ouvertes.fr/hal-02371243
[28]
A. Perquin, G. Lecorvé, D. Lolive, L. Amsaleg.
Évaluation objective de plongements pour la synthèse de parole guidée par réseaux de neurones, in: Traitement automatique du langage naturel, Toulouse, France, July 2019.
https://hal.archives-ouvertes.fr/hal-02419483
[29]
Y. Wang, R. Emonet, E. Fromont, S. Malinowski, E. Menager, L. Mosser, R. Tavenard.
Classification de séries temporelles basée sur des "shapelets" interprétables par réseaux de neurones antagonistes, in: CAp 2019 - Conférence sur l'Apprentissage automatique, Toulouse, France, July 2019, pp. 1-2.
https://hal.archives-ouvertes.fr/hal-02268004

Scientific Books (or Scientific Book chapters)

[30]
Proceedings of the workshop DeFt - Défi Fouille de Textes, July 2019, pp. 1-97.
https://hal.archives-ouvertes.fr/hal-02391768
[31]
L. Amsaleg, O. Chelly, M. E. Houle, K.-I. Kawarabayashi, M. Radovanović, W. Treeratanajaru.
Intrinsic Dimensionality Estimation within Tight Localities, in: Proceedings of the 2019 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, May 2019, pp. 181-189. [ DOI : 10.1137/1.9781611975673.21 ]
https://hal.inria.fr/hal-02125331
[32]
C. Fabre, E. Morin, S. Rosset, P. Sébillot.
Varia - Préface - 60-1, ATALA, January 2020, vol. 60, no 1, pp. 7-11.
https://hal.archives-ouvertes.fr/hal-02433763
[33]
E. Morin, S. Rosset, P. Sébillot.
Varia - Préface - 59-1, ATALA, May 2019, vol. 59, no 1, pp. 7-11.
https://hal.archives-ouvertes.fr/hal-01789046

Books or Proceedings Editing

[34]
L. Amsaleg, B. Huet, M. Larson, G. Gravier, H. Hung, C.-W. Ngo, W. T. Ooi (editors)
Proceedings of the 27th ACM International Conference on Multimedia, ACM Press, Nice, France, October 2019. [ DOI : 10.1145/3343031 ]
https://hal.inria.fr/hal-02378803

Internal Reports

[35]
B. Caramiaux, F. Lotte, J. Geurts, G. Amato, M. Behrmann, F. Bimbot, F. Falchi, A. Garcia, J. Gibert, G. Gravier, H. Holken, H. Koenitz, S. Lefebvre, A. Liutkus, A. Perkis, R. Redondo, E. Turrin, T. Viéville, E. Vincent.
AI in the media and creative industries, New European Media (NEM), April 2019, pp. 1-35, https://arxiv.org/abs/1905.04175.
https://hal.inria.fr/hal-02125504

Other Publications

[36]
A. Antonini, M. Carmen Suárez-Figueroa, A. Adamou, F. Benatti, F. Vignale, G. Gravier, L. Lupi, B. Ouvry-Vial.
Understanding the phenomenology of reading through modelling, 2019, pp. 1-22, Work in progress from the READ-IT program (www.readitproject.eu), Project Leader Brigitte Ouvry-Vial, funded by the EU- Joint Programming Initiative for Cultural Heritage.
https://hal.archives-ouvertes.fr/hal-02305957
[37]
A. Antonini, F. Vignale, G. Gravier, B. Ouvry-Vial.
The Model of Reading : Modelling principles, Definitions, Schema, Alignments, 2019, READ-IT Model of Reading -V2.
https://hal-univ-lemans.archives-ouvertes.fr/hal-02301611
[38]
A. Iscen, G. Tolias, Y. Avrithis, O. Chum.
Label Propagation for Deep Semi-supervised Learning, November 2019, https://arxiv.org/abs/1904.04717 - Accepted to CVPR 2019.
https://hal.inria.fr/hal-02370207
[39]
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
[40]
Y. Lifchitz, Y. Avrithis, S. Picard, A. Bursuc.
Dense Classification and Implanting for Few-Shot Learning, November 2019, https://arxiv.org/abs/1903.05050 - CVPR 2019.
https://hal.inria.fr/hal-02370192
[41]
O. Siméoni, Y. Avrithis, O. Chum.
Local Features and Visual Words Emerge in Activations, November 2019, https://arxiv.org/abs/1905.06358 - working paper or preprint.
https://hal.inria.fr/hal-02370209
[42]
O. Siméoni, M. Budnik, Y. Avrithis, G. Gravier.
Rethinking deep active learning: Using unlabeled data at model training, November 2019, https://arxiv.org/abs/1911.08177 - working paper or preprint.
https://hal.inria.fr/hal-02372102
[43]
Z. Yang, M. Shi, Y. Avrithis, C. Xu, V. Ferrari.
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images, December 2019, https://arxiv.org/abs/1912.00384 - working paper or preprint.
https://hal.inria.fr/hal-02393688
[44]
H. Zhang, Y. Avrithis, T. Furon, L. Amsaleg.
Smooth Adversarial Examples, November 2019, https://arxiv.org/abs/1903.11862 - working paper or preprint.
https://hal.inria.fr/hal-02370202
[45]
H. Zhang, Y. Avrithis, T. Furon, L. Amsaleg.
Walking on the Edge: Fast, Low-Distortion Adversarial Examples, December 2019, https://arxiv.org/abs/1912.02153 - 13 pages, 9 figures.
https://hal.inria.fr/hal-02404216
References in notes
[46]
L. Amsaleg, J. E. Bailey, D. Barbe, S. Erfani, M. E. Houle, V. Nguyen, M. Radovanović.
The Vulnerability of Learning to Adversarial Perturbation Increases with Intrinsic Dimensionality, in: WIFS, 2017.
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L. Amsaleg, O. Chelly, T. Furon, S. Girard, M. E. Houle, K.-I. Kawarabayashi, M. Nett.
Estimating Local Intrinsic Dimensionality, in: KDD, 2015.
[48]
L. Amsaleg, G. Þ. Guðmundsson, B. Þ. Jónsson, M. J. Franklin.
Prototyping a Web-Scale Multimedia Retrieval Service Using Spark, in: ACM TOMCCAP, 2018, vol. 14, no 3s.
[49]
L. Amsaleg, B. Þ. Jónsson, H. Lejsek.
Scalability of the NV-tree: Three Experiments, in: SISAP, 2018.
[50]
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Sketching techniques for very large matrix factorization, in: ECIR, 2016.
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Constraint Satisfaction Programming for Video Summarization, in: ISM, 2013.
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Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning, in: Pattern Recognition, 2018.
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A storytelling machine? : Automatic video summarization: the case of TV series, University of Avignon, France, 2016.
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M. Budnik, M. Demirdelen, G. Gravier.
A Study on Multimodal Video Hyperlinking with Visual Aggregation, in: ICME, 2018.
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N. Carlini, D. A. Wagner.
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text, in: CoRR, 2018, vol. abs/1801.01944.
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R. Carlini Sperandio, S. Malinowski, L. Amsaleg, R. Tavenard.
Time Series Retrieval using DTW-Preserving Shapelets, in: SISAP, 2018.
[58]
V. Claveau, L. E. S. Oliveira, G. Bouzillé, M. Cuggia, C. M. Cabral Moro, N. Grabar.
Numerical eligibility criteria in clinical protocols: annotation, automatic detection and interpretation, in: AIME, 2017.
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A. Delvinioti, H. Jégou, L. Amsaleg, M. E. Houle.
Image Retrieval with Reciprocal and shared Nearest Neighbors, in: VISAPP, 2014.
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H. Farid.
Photo Forensics, The MIT Press, 2016.
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M. Gambhir, V. Gupta.
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I. Goodfellow, Y. Bengio, A. Courville.
Deep Learning, MIT Press, 2016.
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G. Gravier, M. Ragot, L. Amsaleg, R. Bois, G. Jadi, E. Jamet, L. Monceaux, P. Sébillot.
Shaping-Up Multimedia Analytics: Needs and Expectations of Media Professionals, in: MMM, Special Session Perspectives on Multimedia Analytics, 2016.
[64]
A. Iscen, L. Amsaleg, T. Furon.
Scaling Group Testing Similarity Search, in: ICMR, 2016.
[65]
A. Iscen, G. Tolias, Y. Avrithis, O. Chum.
Mining on Manifolds: Metric Learning without Labels, in: CVPR, 2018.
[66]
B. Þ. Jónsson, G. Tómasson, H. Sigurþórsson, Á. Eríksdóttir, L. Amsaleg, M. K. Larusdottir.
A Multi-Dimensional Data Model for Personal Photo Browsing, in: MMM, 2015.
[67]
B. Þ. Jónsson, M. Worring, J. Zahálka, S. Rudinac, L. Amsaleg.
Ten Research Questions for Scalable Multimedia Analytics, in: MMM, Special Session Perspectives on Multimedia Analytics, 2016.
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H. Kim, P. Garrido, A. Tewari, W. Xu, J. Thies, N. Nießner, P. Pérez, C. Richardt, M. Zollhöfer, C. Theobalt.
Deep Video Portraits, in: ACM TOG, 2018.
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M. Laroze, R. Dambreville, C. Friguet, E. Kijak, S. Lefèvre.
Active Learning to Assist Annotation of Aerial Images in Environmental Surveys, in: CBMI, 2018.
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S. Leroux, P. Molchanov, P. Simoens, B. Dhoedt, T. Breuel, J. Kautz.
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification, in: CoRR, 2018, vol. abs/1804.10123.
[71]
A. Lods, S. Malinowski, R. Tavenard, L. Amsaleg.
Learning DTW-Preserving Shapelets, in: IDA, 2017.
[72]
C. Maigrot, E. Kijak, V. Claveau.
Context-Aware Forgery Localization in Social-Media Images: A Feature-Based Approach Evaluation, in: ICIP, 2018.
[73]
D. Shahaf, C. Guestrin.
Connecting the dots between news articles, in: KDD, 2010.
[74]
M. Shi, H. Caesar, V. Ferrari.
Weakly Supervised Object Localization Using Things and Stuff Transfer, in: ICCV, 2017.
[75]
R. Sicre, Y. Avrithis, E. Kijak, F. Jurie.
Unsupervised part learning for visual recognition, in: CVPR, 2017.
[76]
R. Sicre, H. Jégou.
Memory Vectors for Particular Object Retrieval with Multiple Queries, in: ICMR, 2015.
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O. Siméoni, A. Iscen, G. Tolias, Y. Avrithis, O. Chum.
Unsupervised Object Discovery for Instance Recognition, in: WACV, 2018.
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O. Siméoni, A. Iscen, G. Tolias, Y. Avrithis, O. Chum.
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[79]
H. O. Song, Y. Xiang, S. Jegelka, S. Savarese.
Deep Metric Learning via Lifted Structured Feature Embedding, in: CVPR, 2016.
[80]
C. Tsai, M. L. Alexander, N. Okwara, J. R. Kender.
Highly Efficient Multimedia Event Recounting from User Semantic Preferences, in: ICMR, 2014.
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C. B. E. Vaigh, F. Goasdoué, G. Gravier, P. Sébillot.
Using Knowledge Base Semantics in Context-Aware Entity Linking, in: DocEng, 2019.
[82]
O. Vinyals, A. Toshev, S. Bengio, D. Erhan.
Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge, in: TPAMI, 2017, vol. 39, no 4.
[83]
V. Vukotić, C. Raymond, G. Gravier.
Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications, in: ICMR, 2016.
[84]
V. Vukotić, C. Raymond, G. Gravier.
Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking, in: ICMR, 2017.
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V. Vukotić.
Deep Neural Architectures for Automatic Representation Learning from Multimedia Multimodal Data, INSA de Rennes, 2017.
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J. Weston, S. Chopra, A. Bordes.
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H. Yu, J. Wang, Z. Huang, Y. Yang, W. Xu.
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks, in: CVPR, 2016.
[88]
J. Zahálka, M. Worring.
Towards interactive, intelligent, and integrated multimedia analytics, in: VAST, 2014.
[89]
L. Zhang, M. Shi, Q. Chen.
Crowd Counting via Scale-Adaptive Convolutional Neural Network, in: WACV, 2018.
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X. Zhang, X. Zhou, M. Lin, J. Sun.
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, in: CoRR, 2017, vol. abs/1707.01083.
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Provenance filtering for multimedia phylogeny, in: ICIP, 2017.