619 research outputs found
Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability
Video segmentation encompasses a wide range of categories of problem
formulation, e.g., object, scene, actor-action and multimodal video
segmentation, for delineating task-specific scene components with pixel-level
masks. Recently, approaches in this research area shifted from concentrating on
ConvNet-based to transformer-based models. In addition, various
interpretability approaches have appeared for transformer models and video
temporal dynamics, motivated by the growing interest in basic scientific
understanding, model diagnostics and societal implications of real-world
deployment. Previous surveys mainly focused on ConvNet models on a subset of
video segmentation tasks or transformers for classification tasks. Moreover,
component-wise discussion of transformer-based video segmentation models has
not yet received due focus. In addition, previous reviews of interpretability
methods focused on transformers for classification, while analysis of video
temporal dynamics modelling capabilities of video models received less
attention. In this survey, we address the above with a thorough discussion of
various categories of video segmentation, a component-wise discussion of the
state-of-the-art transformer-based models, and a review of related
interpretability methods. We first present an introduction to the different
video segmentation task categories, their objectives, specific challenges and
benchmark datasets. Next, we provide a component-wise review of recent
transformer-based models and document the state of the art on different video
segmentation tasks. Subsequently, we discuss post-hoc and ante-hoc
interpretability methods for transformer models and interpretability methods
for understanding the role of the temporal dimension in video models. Finally,
we conclude our discussion with future research directions
Efficient Human Pose Estimation with Image-dependent Interactions
Human pose estimation from 2D images is one of the most challenging
and computationally-demanding problems in computer vision. Standard
models such as Pictorial Structures consider interactions between
kinematically connected joints or limbs, leading to inference cost
that is quadratic in the number of pixels. As a result, researchers
and practitioners have restricted themselves to simple models which
only measure the quality of limb-pair possibilities by their 2D
geometric plausibility.
In this talk, we propose novel methods which allow for efficient
inference in richer models with data-dependent interactions. First, we
introduce structured prediction cascades, a structured analog of
binary cascaded classifiers, which learn to focus computational effort
where it is needed, filtering out many states cheaply while ensuring
the correct output is unfiltered. Second, we propose a way to
decompose models of human pose with cyclic dependencies into a
collection of tree models, and provide novel methods to impose model
agreement. Finally, we develop a local linear approach that learns
bases centered around modes in the training data, giving us
image-dependent local models which are fast and accurate.
These techniques allow for sparse and efficient inference on the order
of minutes or seconds per image. As a result, we can afford to model
pairwise interaction potentials much more richly with data-dependent
features such as contour continuity, segmentation alignment, color
consistency, optical flow and multiple modes. We show empirically that
these richer models are worthwhile, obtaining significantly more
accurate pose estimation on popular datasets
Smart environment monitoring through micro unmanned aerial vehicles
In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection
Investigating the latency cost of statistical learning of a Gaussian mixture simulating on a convolutional density network with adaptive batch size technique for background modeling
Background modeling is a promising field of study in video analysis, with a wide range of applications in video surveillance. Deep neural networks have proliferated in recent years as a result of effective learning-based approaches to motion analysis. However, these strategies only provide a partial description of the observed scenes' insufficient properties since they use a single-valued mapping to estimate the target background's temporal conditional averages. On the other hand, statistical learning in the imagery domain has become one of the most widely used approaches due to its high adaptability to dynamic context transformation, especially Gaussian Mixture Models. Specifically, these probabilistic models aim to adjust latent parameters to gain high expectation of realistically observed data; however, this approach only concentrates on contextual dynamics in short-term analysis. In a prolonged investigation, it is challenging so that statistical methods cannot reserve the generalization of long-term variation of image data. Balancing the trade-off between traditional machine learning models and deep neural networks requires an integrated approach to ensure accuracy in conception while maintaining a high speed of execution.
In this research, we present a novel two-stage approach for detecting changes using two convolutional neural networks in this work. The first architecture is based on unsupervised Gaussian mixtures statistical learning, which is used to classify the salient features of scenes. The second one implements a light-weighted pipeline of foreground detection. Our two-stage system has a total of approximately 3.5K parameters but still converges quickly to complex motion patterns. Our experiments on publicly accessible datasets demonstrate that our proposed networks are not only capable of generalizing regions of moving objects with promising results in unseen scenarios, but also competitive in terms of performance quality and effectiveness foreground segmentation.
Apart from modeling the data's underlying generator as a non-convex optimization problem, we briefly examine the communication cost associated with the network training by using a distributed scheme of data-parallelism to simulate a stochastic gradient descent algorithm with communication avoidance for parallel machine learnin
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
Graphical models for visual object recognition and tracking
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 277-301).We develop statistical methods which allow effective visual detection, categorization, and tracking of objects in complex scenes. Such computer vision systems must be robust to wide variations in object appearance, the often small size of training databases, and ambiguities induced by articulated or partially occluded objects. Graphical models provide a powerful framework for encoding the statistical structure of visual scenes, and developing corresponding learning and inference algorithms. In this thesis, we describe several models which integrate graphical representations with nonparametric statistical methods. This approach leads to inference algorithms which tractably recover high-dimensional, continuous object pose variations, and learning procedures which transfer knowledge among related recognition tasks. Motivated by visual tracking problems, we first develop a nonparametric extension of the belief propagation (BP) algorithm. Using Monte Carlo methods, we provide general procedures for recursively updating particle-based approximations of continuous sufficient statistics. Efficient multiscale sampling methods then allow this nonparametric BP algorithm to be flexibly adapted to many different applications.(cont.) As a particular example, we consider a graphical model describing the hand's three-dimensional (3D) structure, kinematics, and dynamics. This graph encodes global hand pose via the 3D position and orientation of several rigid components, and thus exposes local structure in a high-dimensional articulated model. Applying nonparametric BP, we recover a hand tracking algorithm which is robust to outliers and local visual ambiguities. Via a set of latent occupancy masks, we also extend our approach to consistently infer occlusion events in a distributed fashion. In the second half of this thesis, we develop methods for learning hierarchical models of objects, the parts composing them, and the scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves accuracy when learning from few examples.(cont.) Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. Adapting these transformed Dirichlet processes to images taken with a binocular stereo camera, we learn integrated, 3D models of object geometry and appearance. This leads to a Monte Carlo algorithm which automatically infers 3D scene structure from the predictable geometry of known object categories.by Erik B. Sudderth.Ph.D
Hidden Markov Models
Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research
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