Deep learning for object detection in robotic grasping contexts

Dans la dernière décennie, les approches basées sur les réseaux de neurones convolutionnels sont devenus les standards pour la plupart des tâches en vision numérique. Alors qu'une grande partie des méthodes classiques de vision étaient basées sur des règles et algorithmes, les réseaux de neurones sont optimisés directement à partir de données d'entraînement qui sont étiquetées pour la tâche voulue. En pratique, il peut être difficile d'obtenir une quantité su sante de données d'entraînement ou d'interpréter les prédictions faites par les réseaux. Également, le processus d'entraînement doit être recommencé pour chaque nouvelle tâche ou ensemble d'objets. Au final, bien que très performantes, les solutions basées sur des réseaux de neurones peuvent être difficiles à mettre en place. Dans cette thèse, nous proposons des stratégies visant à contourner ou solutionner en partie ces limitations en contexte de détection d'instances d'objets. Premièrement, nous proposons d'utiliser une approche en cascade consistant à utiliser un réseau de neurone comme pré-filtrage d'une méthode standard de "template matching". Cette façon de faire nous permet d'améliorer les performances de la méthode de "template matching" tout en gardant son interprétabilité. Deuxièmement, nous proposons une autre approche en cascade. Dans ce cas, nous proposons d'utiliser un réseau faiblement supervisé pour générer des images de probabilité afin d'inférer la position de chaque objet. Cela permet de simplifier le processus d'entraînement et diminuer le nombre d'images d'entraînement nécessaires pour obtenir de bonnes performances. Finalement, nous proposons une architecture de réseau de neurones ainsi qu'une procédure d'entraînement permettant de généraliser un détecteur d'objets à des objets qui ne sont pas vus par le réseau lors de l'entraînement. Notre approche supprime donc la nécessité de réentraîner le réseau de neurones pour chaque nouvel objet.In the last decade, deep convolutional neural networks became a standard for computer vision applications. As opposed to classical methods which are based on rules and hand-designed features, neural networks are optimized and learned directly from a set of labeled training data specific for a given task. In practice, both obtaining sufficient labeled training data and interpreting network outputs can be problematic. Additionnally, a neural network has to be retrained for new tasks or new sets of objects. Overall, while they perform really well, deployment of deep neural network approaches can be challenging. In this thesis, we propose strategies aiming at solving or getting around these limitations for object detection. First, we propose a cascade approach in which a neural network is used as a prefilter to a template matching approach, allowing an increased performance while keeping the interpretability of the matching method. Secondly, we propose another cascade approach in which a weakly-supervised network generates object-specific heatmaps that can be used to infer their position in an image. This approach simplifies the training process and decreases the number of required training images to get state-of-the-art performances. Finally, we propose a neural network architecture and a training procedure allowing detection of objects that were not seen during training, thus removing the need to retrain networks for new objects

Regulation of heat shock factor 1 (HSF1) DNA-binding and transcription

Cellular stress invokes a protective response in which heat shock factor 1 (HSF1) is activated to increase heat shock protein (Hsp) expression. HSF1 exists as a latent monomer in unstressed cells. Upon stress HSF1 forms homotrimers, increasing its affinity for the heat shock DNA element upstream of all Hsp genes. A second conformational change is required for HSF1 to gain transcriptional competence. During prolonged heat shock or following the resumption of normal conditions HSF1 DNA-binding and transcriptional activities are reduced and HSF1 returns to the monomeric state in a process called attenuation. During the activation/deactivation cycle HSF1 is modified by small ubiquitin-related modifier (SUMO-1) conjugation and undergoes several phosphorylation and dephosphorylation events that modulate HSF1 activity. Hyperphosphorylation of HSF1 is hypothesized to trigger HSF1 transcriptional activity. HSF1 also interacts with a dynamic series of Hsp90/Hsp70-based chaperone heterocomplexes that negatively regulate DNA-binding, and transcriptional activity, and promote attenuation. This thesis was aimed at characterizing the mechanisms regulating HSF1 DNA-binding, and transcriptional activity. Expression of human HSF1 in Xenopus oocytes altered the set-point of DNA-binding in response to heat indicating that both the cellular environment and innate properties of the molecule allow HSF1 to set its activation/deactivation set-point in response to stress in vivo. HSF1 DNA-binding but not transcription was activated in oocytes treated with a high temperature heat shock. Further characterization of this observation determined that HSF1 activated by a brief high temperature heat shock inhibited transcriptionally competent HSF1 from activating transcription. It was hypothesized that this phenomenon exists to ensure the eventual death of the cell due to the accumulation of excessive damage and potential mutation caused by severe stress. The most significant observation made in this thesis is that Hsp expression was detected in oocytes injected with reporter plasmid only during recovery from a high temperature heat shock. These results led to the proposal of a model in which HSF1 trimers are either assembled in a transcriptionally incompetent form or one that has the potential to become transcriptionally competent during stress, prior to DNA-binding. The identity of HSF1-binding proteins that interact with HSF1 at different stages of activation/deactivation was characterized in an effort to assign regulatory roles to these proteins. HSF1 was detected in a high molecular weight complex (350-600 kDa) during all phases of the activation/deactivation cycle. HSF1 at different stages of activation was tested for interaction with specific molecular chaperones by electrophoretic mobility supershift analysis. Hsp90, p23, FKBP52, Hip and Hop are all associated with transcriptionally active and inactive HSF1 suggesting that interaction of HSF1 with any of these molecules does not activate HSF1 transcriptional activity. These results do not exclude the possibility that the function of these molecular chaperones may change during activation of HSF1 transcription or that post-translational modifications may be the primary mechanism that drives HSF1 from a transcriptionally inactive to active form