Seminars – 2019

The seminars of OBELIX team are currently held on Mondays 11:00 am, every two weeks, at the IRISA lab, Tohannic campus, room D106.

The seminar is coordinated by Chloé Friguet.

Previous seminars – 2019


  • Date: April, 01
  • Room : D106
  • Speaker: Romaric Gaudel (MCF ENSAI CREST / LACODAM, IRISA, Rennes)
  • Title: Recommendation as a sequential process
  • Abstract: The main track on Recommender Systems (RS) looks for the best next recommendation. At the  opposite, few papers take into account the fact  that a RS handles sequences of recommendations: it  recommends some items  to a user, it gathers the corresponding feedback, it recommends some  other items (to the same or another user), it gathers corresponding  feedback, etc. In my presentation I review a set of  approaches handling two consequences of that aspect of RS: (i) you can reduce computation time by relying on that sequence, (ii) you have to explore to get the best from your RS.

  • Date: April, 05 (14h00 – extra seminar)
  • Room : D106
  • Speaker: Xiaofang Wang (R&D Engineer, INRIA-Rainbow, Rennes)
  • Title:Weakly Supervised Learning for Object Detection
  • Abstract: Machine learning and Deep convolutional networks have become the standard pipeline for a variety of computer vision task such as image classification, object detection, segmentation, etc. These models are known to be data-driven, they requires huge-volume of data labelled by human being or experts. However, manual labeling task is very tedious, time-consuming and expensive, learning from “weaker” annotations (e.g. only image-level category labels to localize object instances by a bounding box) become very interesting to extend the fully-supervised learning algorithms to new applications. In this seminar, I will present two of my methods of weakly supervised learning methods for object detection, including its motivation, originality, evaluation metrics, and future ideas. The first one is to enhancing DPM for weakly supervised learning and The second one is weakly supervised deep learning for crowd detection and counting.

  • Date: March, 20 (14h00 – extra seminar)
  • Room : D106
  • Speaker: Geoffrey Roman-Jimenez (Post-doctoral researcher, Institut de Recherche en Informatique, Toulouse)
  • Title: Science des données et apprentissage statistique pour l’extraction de connaissances à partir de données et d’images
  • Abstract: Dans le cadre de ce séminaire, je présenterai une partie de mes travaux de recherche portant sur l’utilisation de méthodes de sciences des données et d’apprentissage statistique (machine/deep-learning, modélisation/fouille/visualisation de données) pour l’extraction de connaissances à partir de données et d’images. Les premiers travaux que je présenterai porteront sur l’utilisation du transfert d’apprentissage pour la détection de structure au sein de documents manuscrits historiques, en l’absence de vérité-terrain. ’exposerai ensuite mes travaux sur la réidentification de véhicules par l’utilisation des espaces latents de réseaux convolutifs profonds. Je finirai sur la présentation des différents travaux que je mène actuellement sur la modélisation, la fouille et la visualisation de (méta-)données hétérogènes issues des systèmes de vidéoprotection.

  • Date: March, 18 (11h00 – usual seminar)
  • Room : D106
  • Speaker: Yichang Wang (Ph.D. student OBELIX / LACODAM, IRISA, Rennes)
  • Title: Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization
  • Abstract: Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. However, although the learned shapelets are discriminative, they are not always similar to pieces of a real series in the dataset. This makes it difficult to interpret the decision, i.e. difficult to analyze if there are particular behaviors in a series that triggered the decision. In this paper, we make use of a simple convolutional network to tackle the time series classification task and we introduce an adversarial regularization to constrain the model to learn more interpretable shapelets. Our classification results on all the usual time series benchmarks are comparable with the results obtained by similar state-of-the-art algorithms but our adversarially regularized method learns shapelets that are, by design, interpretable.

  • Date: March, 18 (13h00 – extra seminar)
  • Room : A106
  • Speaker: Lina Fahed (Post-doctoral researcher, IMT-Atlantique, Lab-STICC/DECIDE, Brest)
  • Title: Prediction and influence of the occurrence of events in complex sequences
  • Abstract: For several years now, a new phenomenon related to digital data is emerging: data which is increasingly voluminous, varied and rapid, appears and becomes available, it is often referred to as complex data. Complex data is very rich in hidden information, which increases its predictive and detection power. For many years, prediction and detection models in the data mining literature have been proposed for so-called simple data (from a single source, with a clear structure, etc.). However, the generalization and predominance of complex data lead to new challenges, mainly related to taking into account the new characteristics of this data in the modeling process. Consequently, proposing new models adapted to complex data has become essential to better detect, predict and even influence the future, which represents the main challenge that I have chosen to address during my research. This presentation covers my contributions for mining complex data, with a focus on complex sequences, by proposing models for: (i) events prediction, (ii) emergence detection, (iii) events influence and (iv) anomaly detection.

  • Date: March, 11
  • Room : D106
  • Speaker: François Septier (Pr. Statistique / Univ. Bretagne-Sud – Lab-STICC, Vannes)
  • Title: Advanced Monte-Carlo methods for Bayesian filtering
  • Abstract: After a brief description of the general principle of Monte-Carlo methods for Bayesian filtering, I will present some recent contributions in this field. More specifically, I will firstly describe a novel estimator affiliated with the normalized importance sampling estimator but using two proposition densities rather than a single one. I will then present a revisit of the importance sampling with resampling mechanism as a whole in order to produce Monte Carlo samples that are independent, contrary to the classical mechanism, which enables us to develop two new estimators. Finally, I will discuss some sequential adaptations of the Markov Chain Monte Carlo algorithm which represent an interesting alternative to these importance sampling based techniques.

  • Date: February, 25 March, 4
  • Room : D106
  • Speaker: Adan Salazar (Geo-intelligence remote sensing group, Mexico)
  • Title: Challenges and opportunities in relation to Sargassum blooms along the coast of the Mexican Caribbean
  • Abstract: Macroalgal blooms are becoming more frequent events worldwide in response to several factors, some of which have not been proven as responsible for these common events. Reports in the scientific literature for the Atlantic and Pacific coast for Golden tides or  looms of the genus Sargassum have appeared recently. Past and  recent (2011, 2015, 2018) blooms formed by Sargassum species along the Mexican Caribbean coast make us aware  of the necessity to tackle this events not only locally, but at a Regional and Global scale. We have identified at least three species of Sargassum comprising the blooms occurring along the coast of the Mexican Caribbean.  Monthly collections of this biomass derived in taxonomic information, in which other benthic species were also  identified. The chemical characterization of the collected biomass will provide basic information for Sargassum  valorization. Moreover, based on satellite image analysis, we followed the event in order to quantify its pelagic volume and the possible impact in touristic areas and vulnerable ecosystems in nearby coastal area of Quintana  Roo. Basic and applied information on Sargassum biomass may contribute to the understanding of these events  and also suggest management and possible solutions to their widespread occurrence worldwide.

  • Date: February, 11
  • Room : D106
  • Speaker: Marc Russwurm (Ph.D. student / Univ. Munich, Germany)
  • Title: End-to-end Learning for Early Classification of Time Series
  • Abstract: Classification of time series is a topical issue in machine learning. While accuracy stands for the most important evaluation criterion, some applications require decisions to be made as early as possible. Optimization should then target a compromise between earliness, i.e., a capacity of providing a decision early in the sequence, and accuracy. During my stay at IRISA-Obelix, I helped designing a generic, end-to-end trainable framework for early classification of time series. This framework embeds a learnable decision mechanism that can be plugged into a wide range of already existing models. We present results obtained with deep neural networks on a diverse set of time series classification problems. The developed approach compares well to state-of-the-art competitors while being easily adaptable by any existing neural network topology that evaluates a hidden state at each time step.

  • Date: January, 28
  • Room : C011
  • Speaker: Behzad Mirmahboub (post-doc / OBELIX-IRISA, Vannes)
  • Title: Feature Design and Metric Fusion in Person Re-Identification
  • Abstract: Person re-identification is the problem of recognizing a person between several non overlapped cameras. It has important applications in surveillance systems and can reduce human labor and errors of matching persons. Nevertheless, matching two images of a person with different views, poses and illuminations in presence of occlusion and noise is very challenging. Various types of features and metrics are proposed in order to image retrieval for person re-id. In our proposed method we extract different types of features from images and obtain several ranking lists based on their distances. Then, we combine those lists according to their confidence to find the best ranking list.
    Reference: Person re-identification by order-induced metric fusion (Neurocomputing 2018)

 

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