Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.Comment: CVPR 201

Enhancing low-level features with mid-level cues

Local features have become an essential tool in visual recognition. Much of the progress in computer vision over the past decade has built on simple, local representations such as SIFT or HOG. SIFT in particular shifted the paradigm in feature representation. Subsequent works have often focused on improving either computational efficiency, or invariance properties. This thesis belongs to the latter group. Invariance is a particularly relevant aspect if we intend to work with dense features. The traditional approach to sparse matching is to rely on stable interest points, such as corners, where scale and orientation can be reliably estimated, enforcing invariance; dense features need to be computed on arbitrary points. Dense features have been shown to outperform sparse matching techniques in many recognition problems, and form the bulk of our work. In this thesis we present strategies to enhance low-level, local features with mid-level, global cues. We devise techniques to construct better features, and use them to handle complex ambiguities, occlusions and background changes. To deal with ambiguities, we explore the use of motion to enforce temporal consistency with optical flow priors. We also introduce a novel technique to exploit segmentation cues, and use it to extract features invariant to background variability. For this, we downplay image measurements most likely to belong to a region different from that where the descriptor is computed. In both cases we follow the same strategy: we incorporate mid-level, "big picture" information into the construction of local features, and proceed to use them in the same manner as we would the baseline features. We apply these techniques to different feature representations, including SIFT and HOG, and use them to address canonical vision problems such as stereo and object detection, demonstrating that the introduction of global cues yields consistent improvements. We prioritize solutions that are simple, general, and efficient. Our main contributions are as follows: (a) An approach to dense stereo reconstruction with spatiotemporal features, which unlike existing works remains applicable to wide baselines. (b) A technique to exploit segmentation cues to construct dense descriptors invariant to background variability, such as occlusions or background motion. (c) A technique to integrate bottom-up segmentation with recognition efficiently, amenable to sliding window detectors.Les "features" locals s'han convertit en una eina fonamental en el camp del reconeixement visual. Gran part del progrés experimentat en el camp de la visió per computador al llarg de l'última decada es basa en representacions locals de baixa complexitat, com SIFT o HOG. SIFT, en concret, ha canviat el paradigma en representació de característiques visuals. Els treballs que l'han succeït s'acostumen a centrar o bé a millorar la seva eficiencia computacional, o bé propietats d'invariança. El treball presentat en aquesta tesi pertany al segon grup. L'invariança es un aspecte especialment rellevant quan volem treballab amb "features" denses, és a dir per a cada pixel. La manera tradicional d'atacar el problema amb "features" de baixa densitat consisteix en seleccionar punts d'interés estables, com per exemple cantonades, on l'escala i l'orientació poden ser estimades de manera robusta. Les "features" denses, per definició, han de ser calculades en punts arbitraris de la imatge. S'ha demostrat que les "features" denses obtenen millors resultats en tècniques de correspondència per a molts problemes en reconeixement, i formen la major part del nostre treball. En aquesta tesi presentem estratègies per a enriquir "features" locals de baix nivell amb "cues" o dades globals, de mitja complexitat. Dissenyem tècniques per a construïr millors "features", que usem per a atacar problemes tals com correspondències amb un grau elevat d'ambigüetat, oclusions, i canvis del fons de la imatge. Per a atacar ambigüetats, explorem l'ús del moviment per a imposar consistència espai-temporal mitjançant informació d'"optical flow". També presentem una tècnica per explotar dades de segmentació que fem servir per a extreure "features" invariants a canvis en el fons de la imatge. Aquest mètode consisteix en atenuar els components de la imatge (i per tant les "features") que probablement corresponguin a regions diferents a la del descriptor que estem calculant. En ambdós casos seguim la mateixa estratègia: la nostra voluntat és incorporar dades globals d'un nivell de complexitat mitja a la construcció de "features" locals, que procedim a utilitzar de la mateixa manera que les "features" originals. Aquestes tècniques són aplicades a diferents tipus de representacions, incloent SIFT i HOG, i mostrem com utilitzar-les per a atacar problemes fonamentals en visió per computador tals com l'estèreo i la detecció d'objectes. En aquest treball demostrem que introduïnt informació global en la construcció de "features" locals podem obtenir millores consistentment. Donem prioritat a solucions senzilles, generals i eficients. Aquestes són les principals contribucions de la tesi: (a) Una tècnica per a reconstrucció estèreo densa mitjançant "features" espai-temporals, amb l'avantatge respecte a treballs existents que podem aplicar-la a càmeres en qualsevol configuració geomètrica ("wide-baseline"). (b) Una tècnica per a explotar dades de segmentació dins la construcció de descriptors densos, fent-los invariants a canvis al fons de la imatge, i per tant a problemes com les oclusions en estèreo o objectes en moviment. (c) Una tècnica per a integrar segmentació de manera ascendent ("bottom-up") en problemes de reconeixement d'una manera eficient, dissenyada per a detectors de tipus "sliding window"

A PYRAMIDAL APPROACH FOR DESIGNING DEEP NEURAL NETWORK ARCHITECTURES

Developing an intelligent system, capable of learning discriminative high-level features from high dimensional data lies at the core of solving many computer vision (CV ) and machine learning (ML) tasks. Scene or human action recognition from videos is an important topic in CV and ML. Its applications include video surveillance, robotics, human-computer interaction, video retrieval, etc. Several bio inspired hand crafted feature extraction systems have been proposed for processing temporal data. However, recent deep learning techniques have dominated CV and ML by their good performance on large scale datasets. One of the most widely used deep learning technique is Convolutional neural network (CNN) or its variations, e.g. ConvNet, 3DCNN, C3D. CNN kernel scheme reduces the number of parameters with respect to fully connected Neural Networks. Recent deep CNNs have more layers and more kernels for each layer with respect to early CNNs, and as a consequence, they result in a large number of parameters. In addition, they violate the pyramidal plausible architecture of biological neural network due to the increasing number of filters at each higher layer resulting in difficulty for convergence at training step. In this dissertation, we address three main questions central to pyramidal structure and deep neural networks: 1) Is it worth to utilize pyramidal architecture for proposing a generalized recognition system? 2) How to enhance pyramidal neural network (PyraNet) for recognizing action and dynamic scenes in the videos? 3) What will be the impact of imposing pyramidal structure on a deep CNN? In the first part of the thesis, we provide a brief review of the work done for action and dynamic scene recognition using traditional computer vision and machine learning approaches. In addition, we give a historical and present overview of pyramidal neural networks and how deep learning emerged. In the second part, we introduce a strictly pyramidal deep architecture for dynamic scene and human action recognition. It is based on the 3DCNN model and the image pyramid concept. We introduce a new 3D weighting scheme that presents a simple connection scheme with lower computational and memory costs and results in less number of learnable parameters compared to other neural networks. 3DPyraNet extracts features from both spatial and temporal dimensions by keeping biological structure, thereby it is capable to capture the motion information encoded in multiple adjacent frames. 3DPyraNet model is extended with three modifications: 1) changing input image size; 2) changing receptive field and overlap size in correlation layers; and 3) adding a linear classifier at the end to classify the learned features. It results in a discriminative approach for spatiotemporal feature learning in action and dynamic scene recognition. In combination with a linear SVM classifier, our model outperforms state-of-the-art methods in one-vs-all accuracy on three video benchmark datasets (KTH, Weizmann, and Maryland). Whereas, it gives competitive accuracy on a 4th dataset (YUPENN). In the last part of our thesis, we investigate to what extent CNN may take advantage of pyramid structure typical of biological neurons. A generalized statement over convolutional layers from input up-to fully connected layer is introduced that further helps in understanding and designing a successful deep network. It reduces ambiguity, number of parameters, and their size on disk without degrading overall accuracy. It also helps in giving a generalize guideline for modeling a deep architecture by keeping certain ratio of filters in starting layers vs. other deeper layers. Competitive results are achieved compared to similar well-engineered deeper architectures on four benchmark datasets. The same approach is further applied on person re-identification. Less ambiguity in features increase Rank-1 performance and results in better or comparable results to the state-of-the-art deep models

Generalizations of the Multicut Problem for Computer Vision

Graph decomposition has always been a very important concept in machine learning and computer vision. Many tasks like image and mesh segmentation, community detection in social networks, as well as object tracking and human pose estimation can be formulated as a graph decomposition problem. The multicut problem in particular is a popular model to optimize for a decomposition of a given graph. Its main advantage is that no prior knowledge about the number of components or their sizes is required. However, it has several limitations, which we address in this thesis: Firstly, the multicut problem allows to specify only cost or reward for putting two direct neighbours into distinct components. This limits the expressibility of the cost function. We introduce special edges into the graph that allow to define cost or reward for putting any two vertices into distinct components, while preserving the original set of feasible solutions. We show that this considerably improves the quality of image and mesh segmentations. Second, multicut is notorious to be NP-hard for general graphs, that limits its applications to small super-pixel graphs. We define and implement two primal feasible heuristics to solve the problem. They do not provide any guarantees on the runtime or quality of solutions, but in practice show good convergence behaviour. We perform an extensive comparison on multiple graphs of different sizes and properties. Third, we extend the multicut framework by introducing node labels, so that we can jointly optimize for graph decomposition and nodes classification by means of exactly the same optimization algorithm, thus eliminating the need to hand-tune optimizers for a particular task. To prove its universality we applied it to diverse computer vision tasks, including human pose estimation, multiple object tracking, and instance-aware semantic segmentation. We show that we can improve the results over the prior art using exactly the same data as in the original works. Finally, we use employ multicuts in two applications: 1) a client-server tool for interactive video segmentation: After the pre-processing of the video a user draws strokes on several frames and a time-coherent segmentation of the entire video is performed on-the-fly. 2) we formulate a method for simultaneous segmentation and tracking of living cells in microscopy data. This task is challenging as cells split and our algorithm accounts for this, creating parental hierarchies. We also present results on multiple model fitting. We find models in data heavily corrupted by noise by finding components defining these models using higher order multicuts. We introduce an interesting extension that allows our optimization to pick better hyperparameters for each discovered model. In summary, this thesis extends the multicut problem in different directions, proposes algorithms for optimization, and applies it to novel data and settings.Die Zerlegung von Graphen ist ein sehr wichtiges Konzept im maschinellen Lernen und maschinellen Sehen. Viele Aufgaben wie Bild- und Gittersegmentierung, Kommunitätserkennung in sozialen Netzwerken, sowie Objektverfolgung und Schätzung von menschlichen Posen können als Graphzerlegungsproblem formuliert werden. Der Mehrfachschnitt-Ansatz ist ein populäres Mittel um über die Zerlegungen eines gegebenen Graphen zu optimieren. Sein größter Vorteil ist, dass kein Vorwissen über die Anzahl an Komponenten und deren Größen benötigt wird. Dennoch hat er mehrere ernsthafte Limitierungen, welche wir in dieser Arbeit behandeln: Erstens erlaubt der klassische Mehrfachschnitt nur die Spezifikation von Kosten oder Belohnungen für die Trennung von zwei Nachbarn in verschiedene Komponenten. Dies schränkt die Ausdrucksfähigkeit der Kostenfunktion ein und führt zu suboptimalen Ergebnissen. Wir fügen dem Graphen spezielle Kanten hinzu, welche es erlauben, Kosten oder Belohnungen für die Trennung von beliebigen Paaren von Knoten in verschiedene Komponenten zu definieren, ohne die Menge an zulässigen Lösungen zu verändern. Wir zeigen, dass dies die Qualität von Bild- und Gittersegmentierungen deutlich verbessert. Zweitens ist das Mehrfachschnittproblem berüchtigt dafür NP-schwer für allgemeine Graphen zu sein, was die Anwendungen auf kleine superpixel-basierte Graphen einschränkt. Wir definieren und implementieren zwei primal-zulässige Heuristiken um das Problem zu lösen. Diese geben keine Garantien bezüglich der Laufzeit oder der Qualität der Lösungen, zeigen in der Praxis jedoch gutes Konvergenzverhalten. Wir führen einen ausführlichen Vergleich auf vielen Graphen verschiedener Größen und Eigenschaften durch. Drittens erweitern wir den Mehrfachschnitt-Ansatz um Knoten-Kennzeichnungen, sodass wir gemeinsam über Zerlegungen und Knoten-Klassifikationen mit dem gleichen Optimierungs-Algorithmus optimieren können. Dadurch wird der Bedarf der Feinabstimmung einzelner aufgabenspezifischer Löser aus dem Weg geräumt. Um die Allgemeingültigkeit dieses Ansatzes zu überprüfen, haben wir ihn auf verschiedenen Aufgaben des maschinellen Sehens, einschließlich menschliche Posenschätzung, Mehrobjektverfolgung und instanz-bewusste semantische Segmentierung, angewandt. Wir zeigen, dass wir Resultate von vorherigen Arbeiten mit exakt den gleichen Daten verbessern können. Abschließend benutzen wir Mehrfachschnitte in zwei Anwendungen: 1) Ein Nutzer-Server-Werkzeug für interaktive Video Segmentierung: Nach der Vorbearbeitung eines Videos zeichnet der Nutzer Striche auf mehrere Einzelbilder und eine zeit-kohärente Segmentierung des gesamten Videos wird in Echtzeit berechnet. 2) Wir formulieren eine Methode für simultane Segmentierung und Verfolgung von lebenden Zellen in Mikroskopie-Aufnahmen. Diese Aufgabe ist anspruchsvoll, da Zellen sich aufteilen und unser Algorithmus dies in der Erstellung von Eltern-Hierarchien mitberücksichtigen muss. Wir präsentieren außerdem Resultate zur Mehrmodellanpassung. Wir berechnen Modelle in stark verrauschten Daten indem wir mithilfe von Mehrfachschnitten höherer Ordnung Komponenten finden, die diesen Modellen entsprechen. Wir führen eine interessante Erweiterung ein, die es unserer Optimierung erlaubt, bessere Hyperparameter für jedes entdeckte Modell auszuwählen. Zusammenfassend erweitert diese Arbeit den Mehrfachschnitt-Ansatz in unterschiedlichen Richtungen, schlägt Algorithmen zur Inferenz in den resultierenden Modellen vor und wendet ihn auf neuartigen Daten und Umgebungen an

3D data fusion from multiple sensors and its applications

The introduction of depth cameras in the mass market contributed to make computer vision applicable to many real world applications, such as human interaction in virtual environments, autonomous driving, robotics and 3D reconstruction. All these problems were originally tackled by means of standard cameras, but the intrinsic ambiguity in the bidimensional images led to the development of depth cameras technologies. Stereo vision was first introduced to provide an estimate of the 3D geometry of the scene. Structured light depth cameras were developed to use the same concepts of stereo vision but overcome some of the problems of passive technologies. Finally, Time-of-Flight (ToF) depth cameras solve the same depth estimation problem by using a different technology. This thesis focuses on the acquisition of depth data from multiple sensors and presents techniques to efficiently combine the information of different acquisition systems. The three main technologies developed to provide depth estimation are first reviewed, presenting operating principles and practical issues of each family of sensors. The use of multiple sensors then is investigated, providing practical solutions to the problem of 3D reconstruction and gesture recognition. Data from stereo vision systems and ToF depth cameras are combined together to provide a higher quality depth map. A confidence measure of depth data from the two systems is used to guide the depth data fusion. The lack of datasets with data from multiple sensors is addressed by proposing a system for the collection of data and ground truth depth, and a tool to generate synthetic data from standard cameras and ToF depth cameras. For gesture recognition, a depth camera is paired with a Leap Motion device to boost the performance of the recognition task. A set of features from the two devices is used in a classification framework based on Support Vector Machines and Random Forests

The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning

A diverse number of tasks in computer vision and machine learning enjoy from representations of data that are compact yet discriminative, informative and robust to critical measurements. Two notable representations are offered by Region Covariance Descriptors (RCovD) and linear subspaces which are naturally analyzed through the manifold of Symmetric Positive Definite (SPD) matrices and the Grassmann manifold, respectively, two widely used types of Riemannian manifolds in computer vision. As our first objective, we examine image and video-based recognition applications where the local descriptors have the aforementioned Riemannian structures, namely the SPD or linear subspace structure. Initially, we provide a solution to compute Riemannian version of the conventional Vector of Locally aggregated Descriptors (VLAD), using geodesic distance of the underlying manifold as the nearness measure. Next, by having a closer look at the resulting codes, we formulate a new concept which we name Local Difference Vectors (LDV). LDVs enable us to elegantly expand our Riemannian coding techniques to any arbitrary metric as well as provide intrinsic solutions to Riemannian sparse coding and its variants when local structured descriptors are considered. We then turn our attention to two special types of covariance descriptors namely infinite-dimensional RCovDs and rank-deficient covariance matrices for which the underlying Riemannian structure, i.e. the manifold of SPD matrices is out of reach to great extent. %Generally speaking, infinite-dimensional RCovDs offer better discriminatory power over their low-dimensional counterparts. To overcome this difficulty, we propose to approximate the infinite-dimensional RCovDs by making use of two feature mappings, namely random Fourier features and the Nystrom method. As for the rank-deficient covariance matrices, unlike most existing approaches that employ inference tools by predefined regularizers, we derive positive definite kernels that can be decomposed into the kernels on the cone of SPD matrices and kernels on the Grassmann manifolds and show their effectiveness for image set classification task. Furthermore, inspired by attractive properties of Riemannian optimization techniques, we extend the recently introduced Keep It Simple and Straightforward MEtric learning (KISSME) method to the scenarios where input data is non-linearly distributed. To this end, we make use of the infinite dimensional covariance matrices and propose techniques towards projecting on the positive cone in a Reproducing Kernel Hilbert Space (RKHS). We also address the sensitivity issue of the KISSME to the input dimensionality. The KISSME algorithm is greatly dependent on Principal Component Analysis (PCA) as a preprocessing step which can lead to difficulties, especially when the dimensionality is not meticulously set. To address this issue, based on the KISSME algorithm, we develop a Riemannian framework to jointly learn a mapping performing dimensionality reduction and a metric in the induced space. Lastly, in line with the recent trend in metric learning, we devise end-to-end learning of a generic deep network for metric learning using our derivation

Salient Object Detection Techniques in Computer Vision-A Survey.

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end