82,061 research outputs found
Array languages and the N-body problem
This paper is a description of the contributions to the SICSA multicore challenge on many body
planetary simulation made by a compiler group at the University of Glasgow. Our group is part of
the Computer Vision and Graphics research group and we have for some years been developing array
compilers because we think these are a good tool both for expressing graphics algorithms and for
exploiting the parallelism that computer vision applications require.
We shall describe experiments using two languages on two different platforms and we shall compare
the performance of these with reference C implementations running on the same platforms. Finally
we shall draw conclusions both about the viability of the array language approach as compared to
other approaches used in the challenge and also about the strengths and weaknesses of the two, very
different, processor architectures we used
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Object detection systems based on the deep convolutional neural network (CNN)
have recently made ground- breaking advances on several object detection
benchmarks. While the features learned by these high-capacity neural networks
are discriminative for categorization, inaccurate localization is still a major
source of error for detection. Building upon high-capacity CNN architectures,
we address the localization problem by 1) using a search algorithm based on
Bayesian optimization that sequentially proposes candidate regions for an
object bounding box, and 2) training the CNN with a structured loss that
explicitly penalizes the localization inaccuracy. In experiments, we
demonstrated that each of the proposed methods improves the detection
performance over the baseline method on PASCAL VOC 2007 and 2012 datasets.
Furthermore, two methods are complementary and significantly outperform the
previous state-of-the-art when combined.Comment: CVPR 201
Polynomial Triangles Revisited
A polynomial triangle is an array whose inputs are the coefficients in
integral powers of a polynomial. Although polynomial coefficients have appeared
in several works, there is no systematic treatise on this topic. In this paper
we plan to fill this gap. We describe some aspects of these arrays, which
generalize similar properties of the binomial coefficients. Some combinatorial
models enumerated by polynomial coefficients, including lattice paths model,
spin chain model and scores in a drawing game, are introduced. Several known
binomial identities are then extended. In addition, we calculate recursively
generating functions of column sequences. Interesting corollaries follow from
these recurrence relations such as new formulae for the Fibonacci numbers and
Hermite polynomials in terms of trinomial coefficients. Finally, properties of
the entropy density function that characterizes polynomial coefficients in the
thermodynamical limit are studied in details.Comment: 24 pages with 1 figure eps include
Fast Single Shot Detection and Pose Estimation
For applications in navigation and robotics, estimating the 3D pose of
objects is as important as detection. Many approaches to pose estimation rely
on detecting or tracking parts or keypoints [11, 21]. In this paper we build on
a recent state-of-the-art convolutional network for slidingwindow detection
[10] to provide detection and rough pose estimation in a single shot, without
intermediate stages of detecting parts or initial bounding boxes. While not the
first system to treat pose estimation as a categorization problem, this is the
first attempt to combine detection and pose estimation at the same level using
a deep learning approach. The key to the architecture is a deep convolutional
network where scores for the presence of an object category, the offset for its
location, and the approximate pose are all estimated on a regular grid of
locations in the image. The resulting system is as accurate as recent work on
pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly
faster (46 frames per second (FPS) on a TITAN X GPU). This approach to
detection and rough pose estimation is fast and accurate enough to be widely
applied as a pre-processing step for tasks including high-accuracy pose
estimation, object tracking and localization, and vSLAM
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