L’objectif démocratique des politiques d’éducation artistique et culturelle au prisme de l’histoire : une continuité de surface ?

L’objectif démocratique visant à favoriser l’accès aux arts et à la culture pour tous les élèves est l’un des leitmotivs les plus stables des politiques d’éducation artistique des cinquante dernières années en France. Rarement un objectif éducatif, par ailleurs fortement chevillé à des valeurs politiques, n’a bénéficié d’une telle longévité et suscité autant d’unanimité dans l’histoire du système éducatif français. L’article formule et met à l’épreuve l’hypothèse selon laquelle, sous l’impression de continuité produite par la mise à l’avant-plan d’enjeux démocratiques, les objectifs de l’éducation artistique et culturelle subissent un ensemble de réorientations discrètes, décelables par l’analyse qualitative de l’évolution des champs lexicaux et sémantiques mobilisés dans les discours produits sur l’éducation artistique et culturelle, et par leur mise en regard avec les contextes tout à la fois sociaux et éducatifs (particulièrement sur le plan des politiques éducatives) constituant l’arrière-plan de leur production. La recherche repose sur le recensement, l’analyse et la mise en regard de deux corpus de textes (archives constituées de textes de lois et des discours officiels qui les accompagnent) renvoyant : 1/ à la mise en œuvre des politiques d’éducation artistique et culturelle en France depuis 1968 d’une part ; 2/ à l’évolution concomitante des politiques éducatives au sein du même espace d’autre part

Model-based inexact graph matching on top of DNNs for semantic scene understanding

27 pages, 9 figures, 11 tablesInternational audienceDeep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results provided by deep learning. This module corresponds to a "many-to-one-or-none" inexact graph matching approach, and is formulated as a quadratic assignment problem. Our approach is compared to a CNN-based segmentation (for various CNN backbones) on two public datasets, one for face segmentation from 2D RGB images (FASSEG), and the other for brain segmentation from 3D MRIs (IBSR). Evaluations are performed using two types of structural information (distances and directional relations, , this choice being a hyper-parameter of our generic framework). On FASSEG data, results show that our module improves accuracy of the CNN by about 6.3% (the Hausdorff distance decreases from 22.11 to 20.71). On IBSR data, the improvement is of 51% (the Hausdorff distance decreases from 11.01 to 5.4). In addition, our approach is shown to be resilient to small training datasets that often limit the performance of deep learning methods: the improvement increases as the size of the training dataset decreases

QAP Optimisation with Reinforcement Learning for Faster Graph Matching in Sequential Semantic Image Analysis

International audienceThe paper addresses the fundamental task of semantic image analysis by exploiting structural information (spatial relationshipsbetween image regions). We propose to combine a deep neural network(CNN) with graph matching where graphs encode efficiently structuralinformation related to regions segmented by the CNN. Our novel approach solves the quadratic assignment problem (QAP) sequentially formatching graphs. The optimal sequence for graph matching is conveniently defined using reinforcement-learning (RL) based on the regionmembership probabilities produced by the CNN and their structural relationships. Our RL-based strategy for solving QAP sequentially allowsus to significantly reduce the combinatorial complexity for graph matching. Preliminary experiments are performed on both a synthetic datasetand a public dataset dedicated to the semantic segmentation of face images. Results show that the proposed RL-based ordering significantlyoutperforms random ordering and that our strategy is about 386 timesfaster than a global QAP-based approach while preserving similar segmentation accuracy

Improving semantic segmentation with graph-based structural knowledge

International audienceDeep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results provided by deep learning. This module corresponds to a ``many-to-one-or-none'' inexact graph matching approach, and is formulated as a quadratic assignment problem. Using two standard measures for evaluation, we show experimentally that our pipeline for segmentation of 3D MRI data of the brain outperforms the baseline CNN (U-Net) used alone. In addition, our approach is shown to be resilient to small training datasets that often limit the performance of deep learning

Improving semantic segmentation with graph-based structural knowledge

International audienceDeep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results provided by deep learning. This module corresponds to a ``many-to-one-or-none'' inexact graph matching approach, and is formulated as a quadratic assignment problem. Using two standard measures for evaluation, we show experimentally that our pipeline for segmentation of 3D MRI data of the brain outperforms the baseline CNN (U-Net) used alone. In addition, our approach is shown to be resilient to small training datasets that often limit the performance of deep learning

Improving semantic segmentation with graph-based structural knowledge

International audienceDeep learning based pipelines for semantic segmentation often ignore structural information available on annotated images used for training. We propose a novel post-processing module enforcing structural knowledge about the objects of interest to improve segmentation results provided by deep learning. This module corresponds to a ``many-to-one-or-none'' inexact graph matching approach, and is formulated as a quadratic assignment problem. Using two standard measures for evaluation, we show experimentally that our pipeline for segmentation of 3D MRI data of the brain outperforms the baseline CNN (U-Net) used alone. In addition, our approach is shown to be resilient to small training datasets that often limit the performance of deep learning

Apprentissage profond et localisation d’objets : deux exemples d’applications en agriculture

International audienc

Apprentissage profond et localisation d’objets : deux exemples d’applications en agriculture

International audienc

ProNGF increases breast tumor aggressiveness through functional association of TrkA with EphA2

ProNGF expression has been linked to several types of cancers including breast cancer, and we have previously shown that proNGF stimulates breast cancer invasion in an autocrine manner through membrane receptors sortilin and TrkA. However, little is known regarding TrkA-associated protein partners upon proNGF stimulation. By proteomic analysis and proximity ligation assays, we found that proNGF binding to sortilin induced sequential formation of the functional sortilin/TrkA/EphA2 complex, leading to TrkA-phosphorylation dependent Akt activation and EphA2-dependent Src activation. EphA2 inhibition using siRNA approach abolished proNGF-stimulated clonogenic growth of breast cancer cell lines. Combinatorial targeting of TrkA and EphA2 dramatically reduced colony formation in vitro, primary tumor growth and metastatic dissemination towards the brain in vivo. Finally, proximity ligation assay in breast tumor samples revealed that increased TrkA/EphA2 proximity ligation assay signals were correlated with a decrease of overall survival in patients. All together, these data point out the importance of TrkA/EphA2 functional association in proNGF-induced tumor promoting effects, and provide a rationale to target proNGF/TrkA/EphA2 axis by alternative methods other than the simple use of tyrosine kinase inhibitors in breast cancer