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Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation...
One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown...
The long-tail phenomenon tells us that there are many items in the tail. However, not all tail items are the same. Each item acquires different kinds of users. Some items are loved by the general public, while some items are consumed by eccentric fans. In this paper, we propose a novel metric, item eccentricity, to incorporate this difference between consumers of the items. Eccentric items are defined...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
This paper addresses the problem of defocus map estimation from a single image. We present a fast yet effective approach to estimate the spatially varying amounts of defocus blur at edge locations, which is based on the maximum ranks of the corresponding local patches with different orientations in gradient domain. Such an approach is motivated by the theoretical analysis which reveals the connection...
A kernel or mini-app is a self-contained small application that retains certain characteristics of the original application [7]. Working on a kernel or mini-app in the place of the original application can dramatically reduce the resources and effort required for performing software tasks such as performance optimization and porting to new platforms. However, using kernel as a proxy is based on the...
In this paper we present lo2s - a lightweight performance monitoring tool to sample applications as well as the executing system. It enables the user to analyze the performance of a parallel application without requiring the time-consuming and error-prone process of application instrumentation. The collected performance data is complemented with various metric data, i.e., perf counters, kernel tracepoints,...
Widely used benchmarks, such as High Performance Linpack (HPL), do not always provide direct insights are notoriously poor indicators of into the actual application performance of systems. When real applications are used, and there have been are criticisms indicating that the performance of simplified benchmarks such as HPL no longer strongly correlate to real application performance. In contrast,...
Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These...
This paper presents the details of a CUDA implementation of the Subgraph Isomorphism Graph Challenge, a new effort aimed at driving progress in the graph analytics field. challenge consists of two graph analytics: triangle counting and k-truss. We present our CUDA implementation of the graph triangle counting operation and of the k-truss subgraph decomposition. Both implementations share the same...
The problem of image enhancement for low-contrast images is considered. The histogram-based method of automatic contrast enhancement on the basis of the analyzing of contrast distribution at the boundaries of low-contrast image elements (objects and background) using the various definitions of contrast kernels is proposed. The research of the effectiveness of the proposed and the well-known histogram-based...
The problem of measuring the contrast of image elements (objects and background) for complex monochrome images is considered in this paper. A new method for measuring the contrast of image elements is proposed on the basis of analytical assessments of the contrast of the corresponding elements of the initial and inverted images. The new definitions for weighted and relative contrast of image elements...
The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges to move these communities forward. The...
Experiment-based black-box optimization is now under active research, because of its usefulness in a wide range of fields. Among related studies, some proposed an iterative algorithm for optimizing environmentally adaptive control policies using response surface method, which is expected to be useful for the applications in, e.g. mobile robots. A metric named unbiased expected improvement is the key...
In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based...
Incremental learning allows incorporating new data in a classifier model without full retraining for computational efficiency. In this paper, we present two ways of performing incremental learning on Grassmann manifolds. In a Grassmann kernel learning framework, data are embedded on subspaces and kernels are constructed to map data subspaces to a projection space for classification. As new data samples...
In Gradient-Based Cross-Spectral Stereo Matching (GB-CSSM) output disparity maps tend to produce coarse results that are, for the most part, reliable. However, general methods of improving the performance of disparity maps generated from the Cross-Spectral comparison of visual and full infrared input images are non-existent. In particular, previous works fail to address the role and interaction of...
We present a novel online learning paradigm for nonlinear function estimation based on iterative orthogonal projections in an L2 space reflecting the stochastic property of input signals. An online algorithm is built upon the fact that any finite dimensional subspace has a reproducing kernel, which is given in terms of the Gram matrix of its basis. The basis used in the present study involves multiple...
Optimizing the performance of GPU kernels is challenging for both human programmers and code generators. For example, CUDA programmers must set thread and block parameters for a kernel, but might not have the intuition to make a good choice. Similarly, compilers can generate working code, but may miss tuning opportunities by not targeting GPU models or performing code transformations. Although empirical...
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