Video alignment using unsupervised learning of local and global features

In this paper, we tackle the problem of video alignment, the process of matching the frames of a pair of videos containing similar actions. The main challenge in video alignment is that accurate correspondence should be established despite the differences in the execution processes and appearances between the two videos. We introduce an unsupervised method for alignment that uses global and local features of the frames. In particular, we introduce effective features for each video frame by means of three machine vision tools: person detection, pose estimation, and VGG network. Then the features are processed and combined to construct a multidimensional time series that represent the video. The resulting time series are used to align videos of the same actions using a novel version of dynamic time warping named Diagonalized Dynamic Time Warping(DDTW). The main advantage of our approach is that no training is required, which makes it applicable for any new type of action without any need to collect training samples for it. For evaluation, we considered video synchronization and phase classification tasks on the Penn action dataset. Also, for an effective evaluation of the video synchronization task, we present a new metric called Enclosed Area Error(EAE). The results show that our method outperforms previous state-of-the-art methods, such as TCC and other self-supervised and supervised methods.Comment: 10 pages, 7 figure

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Learning representations of multivariate time series with missing data

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordLearning compressed representations of multivariate time series (MTS) facilitates data analysis in the presence of noise and redundant information, and for a large number of variates and time steps. However, classical dimensionality reduction approaches are designed for vectorial data and cannot deal explicitly with missing values. In this work, we propose a novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS. The proposed model can process inputs characterized by variable lengths and it is specifically designed to handle missing data. Our autoencoder learns fixed-length vectorial representations, whose pairwise similarities are aligned to a kernel function that operates in input space and that handles missing values. This allows to learn good representations, even in the presence of a significant amount of missing data. To show the effectiveness of the proposed approach, we evaluate the quality of the learned representations in several classification tasks, including those involving medical data, and we compare to other methods for dimensionality reduction. Successively, we design two frameworks based on the proposed architecture: one for imputing missing data and another for one-class classification. Finally, we analyze under what circumstances an autoencoder with recurrent layers can learn better compressed representations of MTS than feed-forward architectures.Norwegian Research Counci

The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements

Truly real-life data presents a strong, but exciting challenge for sentiment and emotion research. The high variety of possible `in-the-wild' properties makes large datasets such as these indispensable with respect to building robust machine learning models. A sufficient quantity of data covering a deep variety in the challenges of each modality to force the exploratory analysis of the interplay of all modalities has not yet been made available in this context. In this contribution, we present MuSe-CaR, a first of its kind multimodal dataset. The data is publicly available as it recently served as the testing bed for the 1st Multimodal Sentiment Analysis Challenge, and focused on the tasks of emotion, emotion-target engagement, and trustworthiness recognition by means of comprehensively integrating the audio-visual and language modalities. Furthermore, we give a thorough overview of the dataset in terms of collection and annotation, including annotation tiers not used in this year's MuSe 2020. In addition, for one of the sub-challenges - predicting the level of trustworthiness - no participant outperformed the baseline model, and so we propose a simple, but highly efficient Multi-Head-Attention network that exceeds using multimodal fusion the baseline by around 0.2 CCC (almost 50 % improvement).Comment: accepted versio

Applications of stochastic analysis and algebra to machine learning

In this thesis we consider the application of tools from stochastic analysis and algebra to statistics and machine learning. Most of these tools are different forms of what has become known as signature methods. The signature has been discovered and rediscovered in a few different areas of mathematics in the last 70 years. In short, it maps a path evolving in a vector space to a group enveloping that same space. The reason it is so useful for statistics is twofold: One, its set of invariances is highly desirable in many applications, and two, its image group is highly structured making it particularly amenable to mathematical study using algebraic tools. The primary aim here is to study how one may use the signature to express statistical properties of a given path, and how these properties can be applied to machine learning. This aim manifests itself as - among other things: - a new type of cumulants for signatures that have unique combinatorial properties and can be used to characterise independence of paths, - cumulants on reproducing kernel Hilbert spaces which are related to the signature cumulants, even though signatures are not used explicitly, - a generalisation of the signature to other types of feature maps into non-commutative algebras, - a feature map with an initial topology that captures properties of the filtration of stochastic processes, - and a family of scoring rules with associated divergences, entropies and mutual informations for paths that respect their group structure. These are divided into separate, self contained chapters that can be read independently of one another

Robots that Learn and Plan — Unifying Robot Learning and Motion Planning for Generalized Task Execution

Robots have the potential to assist people with a variety of everyday tasks, but to achieve that potential robots require software capable of planning and executing motions in cluttered environments. To address this, over the past few decades, roboticists have developed numerous methods for planning motions to avoid obstacles with increasingly stronger guarantees, from probabilistic completeness to asymptotic optimality. Some of these methods have even considered the types of constraints that must be satisfied to perform useful tasks, but these constraints must generally be manually specified. In recent years, there has been a resurgence of methods for automatic learning of tasks from human-provided demonstrations. Unfortunately, these two fields, task learning and motion planning, have evolved largely separate from one another, and the learned models are often not usable by motion planners. In this thesis, we aim to bridge the gap between robot task learning and motion planning by employing a learned task model that can subsequently be leveraged by an asymptotically-optimal motion planner to autonomously execute the task. First, we show that application of a motion planner enables task performance while avoiding novel obstacles and extend this to dynamic environments by replanning at reactive rates. Second, we generalize the method to accommodate time-invariant model parameters, allowing more information to be gleaned from the demonstrations. Third, we describe a more principled approach to temporal registration for such learning methods that mirrors the ultimate integration with a motion planner and often reduces the number of demonstrations required. Finally, we extend this framework to the domain of mobile manipulation. We empirically evaluate each of these contributions on multiple household tasks using the Aldebaran Nao, Rethink Robotics Baxter, and Fetch mobile manipulator robots to show that these approaches improve task execution success rates and reduce the amount of human-provided information required.Doctor of Philosoph

Correspondence problems in computer vision : novel models, numerics, and applications

Correspondence problems like optic flow belong to the fundamental problems in computer vision. Here, one aims at finding correspondences between the pixels in two (or more) images. The correspondences are described by a displacement vector field that is often found by minimising an energy (cost) function. In this thesis, we present several contributions to the energy-based solution of correspondence problems: (i) We start by developing a robust data term with a high degree of invariance under illumination changes. Then, we design an anisotropic smoothness term that works complementary to the data term, thereby avoiding undesirable interference. Additionally, we propose a simple method for determining the optimal balance between the two terms. (ii) When discretising image derivatives that occur in our continuous models, we show that adapting one-sided upwind discretisations from the field of hyperbolic differential equations can be beneficial. To ensure a fast solution of the nonlinear system of equations that arises when minimising the energy, we use the recent fast explicit diffusion (FED) solver in an explicit gradient descent scheme. (iii) Finally, we present a novel application of modern optic flow methods where we align exposure series used in high dynamic range (HDR) imaging. Furthermore, we show how the alignment information can be used in a joint super-resolution and HDR method.Korrespondenzprobleme wie der optische Fluß, gehören zu den fundamentalen Problemen im Bereich des maschinellen Sehens (Computer Vision). Hierbei ist das Ziel, Korrespondenzen zwischen den Pixeln in zwei (oder mehreren) Bildern zu finden. Die Korrespondenzen werden durch ein Verschiebungsvektorfeld beschrieben, welches oft durch Minimierung einer Energiefunktion (Kostenfunktion) gefunden wird. In dieser Arbeit stellen wir mehrere Beiträge zur energiebasierten Lösung von Korrespondenzproblemen vor: (i) Wir beginnen mit der Entwicklung eines robusten Datenterms, der ein hohes Maß an Invarianz unter Beleuchtungsänderungen aufweißt. Danach entwickeln wir einen anisotropen Glattheitsterm, der komplementär zu dem Datenterm wirkt und deshalb keine unerwünschten Interferenzen erzeugt. Zusätzlich schlagen wir eine einfache Methode vor, die es erlaubt die optimale Balance zwischen den beiden Termen zu bestimmen. (ii) Im Zuge der Diskretisierung von Bildableitungen, die in unseren kontinuierlichen Modellen auftauchen, zeigen wir dass es hilfreich sein kann, einseitige upwind Diskretisierungen aus dem Bereich hyperbolischer Differentialgleichungen zu übernehmen. Um eine schnelle Lösung des nichtlinearen Gleichungssystems, dass bei der Minimierung der Energie auftaucht, zu gewährleisten, nutzen wir den kürzlich vorgestellten fast explicit diffusion (FED) Löser im Rahmen eines expliziten Gradientenabstiegsschemas. (iii) Schließlich stellen wir eine neue Anwendung von modernen optischen Flußmethoden vor, bei der Belichtungsreihen für high dynamic range (HDR) Bildgebung registriert werden. Außerdem zeigen wir, wie diese Registrierungsinformation in einer kombinierten super-resolution und HDR Methode genutzt werden kann

SPATIO-TEMPORAL REGISTRATION IN AUGMENTED REALITY

The overarching goal of Augmented Reality (AR) is to provide users with the illusion that virtual and real objects coexist indistinguishably in the same space. An effective persistent illusion requires accurate registration between the real and the virtual objects, registration that is spatially and temporally coherent. However, visible misregistration can be caused by many inherent error sources, such as errors in calibration, tracking, and modeling, and system delay. This dissertation focuses on new methods that could be considered part of "the last mile" of spatio-temporal registration in AR: closed-loop spatial registration and low-latency temporal registration: 1. For spatial registration, the primary insight is that calibration, tracking and modeling are means to an end---the ultimate goal is registration. In this spirit I present a novel pixel-wise closed-loop registration approach that can automatically minimize registration errors using a reference model comprised of the real scene model and the desired virtual augmentations. Registration errors are minimized in both global world space via camera pose refinement, and local screen space via pixel-wise adjustments. This approach is presented in the context of Video See-Through AR (VST-AR) and projector-based Spatial AR (SAR), where registration results are measurable using a commodity color camera. 2. For temporal registration, the primary insight is that the real-virtual relationships are evolving throughout the tracking, rendering, scanout, and display steps, and registration can be improved by leveraging fine-grained processing and display mechanisms. In this spirit I introduce a general end-to-end system pipeline with low latency, and propose an algorithm for minimizing latency in displays (DLP DMD projectors in particular). This approach is presented in the context of Optical See-Through AR (OST-AR), where system delay is the most detrimental source of error. I also discuss future steps that may further improve spatio-temporal registration. Particularly, I discuss possibilities for using custom virtual or physical-virtual fiducials for closed-loop registration in SAR. The custom fiducials can be designed to elicit desirable optical signals that directly indicate any error in the relative pose between the physical and projected virtual objects.Doctor of Philosoph

DEFORM'06 - Proceedings of the Workshop on Image Registration in Deformable Environments

Preface These are the proceedings of DEFORM'06, the Workshop on Image Registration in Deformable Environments, associated to BMVC'06, the 17th British Machine Vision Conference, held in Edinburgh, UK, in September 2006. The goal of DEFORM'06 was to bring together people from different domains having interests in deformable image registration. In response to our Call for Papers, we received 17 submissions and selected 8 for oral presentation at the workshop. In addition to the regular papers, Andrew Fitzgibbon from Microsoft Research Cambridge gave an invited talk at the workshop. The conference website including online proceedings remains open, see http://comsee.univ-bpclermont.fr/events/DEFORM06. We would like to thank the BMVC'06 co-chairs, Mike Chantler, Manuel Trucco and especially Bob Fisher for is great help in the local arrangements, Andrew Fitzgibbon, and the Programme Committee members who provided insightful reviews of the submitted papers. Special thanks go to Marc Richetin, head of the CNRS Research Federation TIMS, which sponsored the workshop. August 2006 Adrien Bartoli Nassir Navab Vincent Lepeti