Learning visual representations with neural networks for video captioning and image generation

La recherche sur les réseaux de neurones a permis de réaliser de larges progrès durant la dernière décennie. Non seulement les réseaux de neurones ont été appliqués avec succès pour résoudre des problèmes de plus en plus complexes; mais ils sont aussi devenus l’approche dominante dans les domaines où ils ont été testés tels que la compréhension du langage, les agents jouant à des jeux de manière automatique ou encore la vision par ordinateur, grâce à leurs capacités calculatoires et leurs efficacités statistiques. La présente thèse étudie les réseaux de neurones appliqués à des problèmes en vision par ordinateur, où les représentations sémantiques abstraites jouent un rôle fondamental. Nous démontrerons, à la fois par la théorie et par l’expérimentation, la capacité des réseaux de neurones à apprendre de telles représentations à partir de données, avec ou sans supervision. Le contenu de la thèse est divisé en deux parties. La première partie étudie les réseaux de neurones appliqués à la description de vidéo en langage naturel, nécessitant l’apprentissage de représentation visuelle. Le premier modèle proposé permet d’avoir une attention dynamique sur les différentes trames de la vidéo lors de la génération de la description textuelle pour de courtes vidéos. Ce modèle est ensuite amélioré par l’introduction d’une opération de convolution récurrente. Par la suite, la dernière section de cette partie identifie un problème fondamental dans la description de vidéo en langage naturel et propose un nouveau type de métrique d’évaluation qui peut être utilisé empiriquement comme un oracle afin d’analyser les performances de modèles concernant cette tâche. La deuxième partie se concentre sur l’apprentissage non-supervisé et étudie une famille de modèles capables de générer des images. En particulier, l’accent est mis sur les “Neural Autoregressive Density Estimators (NADEs), une famille de modèles probabilistes pour les images naturelles. Ce travail met tout d’abord en évidence une connection entre les modèles NADEs et les réseaux stochastiques génératifs (GSN). De plus, une amélioration des modèles NADEs standards est proposée. Dénommés NADEs itératifs, cette amélioration introduit plusieurs itérations lors de l’inférence du modèle NADEs tout en préservant son nombre de paramètres. Débutant par une revue chronologique, ce travail se termine par un résumé des récents développements en lien avec les contributions présentées dans les deux parties principales, concernant les problèmes d’apprentissage de représentation sémantiques pour les images et les vidéos. De prometteuses directions de recherche sont envisagées.The past decade has been marked as a golden era of neural network research. Not only have neural networks been successfully applied to solve more and more challenging real- world problems, but also they have become the dominant approach in many of the places where they have been tested. These places include, for instance, language understanding, game playing, and computer vision, thanks to neural networks’ superiority in computational efficiency and statistical capacity. This thesis applies neural networks to problems in computer vision where high-level and semantically meaningful representations play a fundamental role. It demonstrates both in theory and in experiment the ability to learn such representations from data with and without supervision. The main content of the thesis is divided into two parts. The first part studies neural networks in the context of learning visual representations for the task of video captioning. Models are developed to dynamically focus on different frames while generating a natural language description of a short video. Such a model is further improved by recurrent convolutional operations. The end of this part identifies fundamental challenges in video captioning and proposes a new type of evaluation metric that may be used experimentally as an oracle to benchmark performance. The second part studies the family of models that generate images. While the first part is supervised, this part is unsupervised. The focus of it is the popular family of Neural Autoregressive Density Estimators (NADEs), a tractable probabilistic model for natural images. This work first makes a connection between NADEs and Generative Stochastic Networks (GSNs). The standard NADE is improved by introducing multiple iterations in its inference without increasing the number of parameters, which is dubbed iterative NADE. With a historical view at the beginning, this work ends with a summary of recent development for work discussed in the first two parts around the central topic of learning visual representations for images and videos. A bright future is envisioned at the end

Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates

Despite the recent success of deep learning, the mammalian brain is still unrivaled when it comes to interpreting complex, high-dimensional data streams like visual, auditory and somatosensory stimuli. However, the underlying computational principles allowing the brain to deal with unreliable, high-dimensional and often incomplete data while having a power consumption on the order of a few watt are still mostly unknown. In this work, we investigate how specific functionalities emerge from simple structures observed in the mammalian cortex, and how these might be utilized in non-von Neumann devices like “neuromorphic hardware”. Firstly, we show that an ensemble of deterministic, spiking neural networks can be shaped by a simple, local learning rule to perform sampling-based Bayesian inference. This suggests a coding scheme where spikes (or “action potentials”) represent samples of a posterior distribution, constrained by sensory input, without the need for any source of stochasticity. Secondly, we introduce a top-down framework where neuronal and synaptic dynamics are derived using a least action principle and gradient-based minimization. Combined, neurosynaptic dynamics approximate real-time error backpropagation, mappable to mechanistic components of cortical networks, whose dynamics can again be described within the proposed framework. The presented models narrow the gap between well-defined, functional algorithms and their biophysical implementation, improving our understanding of the computational principles the brain might employ. Furthermore, such models are naturally translated to hardware mimicking the vastly parallel neural structure of the brain, promising a strongly accelerated and energy-efficient implementation of powerful learning and inference algorithms, which we demonstrate for the physical model system “BrainScaleS–1”

Tensor Regression

Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies such as neuroimaging, computer vision, climatology and social networks, has brought challenges to traditional data representation methods. Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data. In this book, the authors provide a systematic study and analysis of tensor-based regression models and their applications in recent years. It groups and illustrates the existing tensor-based regression methods and covers the basics, core ideas, and theoretical characteristics of most tensor-based regression methods. In addition, readers can learn how to use existing tensor-based regression methods to solve specific regression tasks with multiway data, what datasets can be selected, and what software packages are available to start related work as soon as possible. Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis. It is essential reading for all students, researchers and practitioners of working on high dimensional data.Comment: 187 pages, 32 figures, 10 table

Temporal Segmentation of Human Motion for Rehabilitation

Current physiotherapy practice relies on visual observation of patient movement for assessment and diagnosis. Automation of motion monitoring has the potential to improve accuracy and reliability, and provide additional diagnostic insight to the clinician, improving treatment quality, and patient progress. To enable automated monitoring, assessment, and diagnosis, the movements of the patient must be temporally segmented from the continuous measurements. Temporal segmentation is the process of identifying the starting and ending locations of movement primitives in a time-series data sequence. Most segmentation algorithms require training data, but a priori knowledge of the patient's movement patterns may not be available, necessitating the use of healthy population data for training. However, healthy population movement data may not generalize well to rehabilitation patients due to large differences in motion characteristics between the two demographics. In this thesis, four key contributions will be elaborated to enable accurate segmentation of patient movement data during rehabilitation. The first key contribution is the creation of a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application specific requirements, algorithm mechanics, and validation techniques. This framework provides a structure for considering the factors that must be incorporated when constructing a segmentation and identification algorithm. The framework enables systematic comparison of different segmentation algorithms, provides the means to examine the impact of each algorithm component, and allows for a systematic approach to determine the best algorithm for a given situation. The second key contribution is the development of an online and accurate motion segmentation algorithm based on a classification framework. The proposed algorithm transforms the segmentation task into a classification problem by modelling the segment edge point directly. Given this formulation, a variety of feature transformation, dimensionality reduction and classifier techniques were investigated on several healthy and patient datasets. With proper normalization, the segmentation algorithm can be trained using healthy participant data and obtain high quality segments on patient data. Inter-participant and inter-primitive variability were assessed on a dataset of 30 healthy participants and 44 rehabilitation participants, demonstrating the generalizability and utility of the proposed approach for rehabilitation settings. The proposed approach achieves a segmentation accuracy of 83-100%. The third key contribution is the investigation of feature set generalizability of the proposed method. Nearly all segmentation techniques developed previously use a single sensor modality. The proposed method was applied to joint angles, electromyogram, motion capture, and force plate data to investigate how the choice of modality impacts segmentation performance. With proper normalization, the proposed method was shown to work with various input sensor types and achieved high accuracy on all sensor modalities examined. The proposed approach achieves a segmentation accuracy of 72-97%. The fourth key contribution is the development of a new feature set based on hypotheses about the optimality of human motion trajectory generation. A common hypothesis in human motor control is that human movement is generated by optimizing with respect to a certain criterion and is task dependent. In this thesis, a method to segment human movement by detecting changes to the optimization criterion being used via inverse trajectory optimization is proposed. The control strategy employed by the motor system is hypothesized to be a weighted sum of basis cost functions, with the basis weights changing with changes to the motion objective(s). Continuous time series data of movement is processed using a sliding fixed width window, estimating the basis weights of each cost function for each window by minimizing the Karush-Kuhn-Tucker optimality conditions. The quality of the cost function recovery is verified by evaluating the residual. The successfully estimated basis weights are averaged together to create a set of time varying basis weights that describe the changing control strategy of the motion and can be used to segment the movement with simple thresholds. The proposed algorithm is first demonstrated on simulation data and then demonstrated on a dataset of human subjects performing a series of exercise tasks. The proposed approach achieves a segmentation accuracy of 74-88%