51,424 research outputs found
High efficiency compression for object detection
Image and video compression has traditionally been tailored to human vision.
However, modern applications such as visual analytics and surveillance rely on
computers seeing and analyzing the images before (or instead of) humans. For
these applications, it is important to adjust compression to computer vision.
In this paper we present a bit allocation and rate control strategy that is
tailored to object detection. Using the initial convolutional layers of a
state-of-the-art object detector, we create an importance map that can guide
bit allocation to areas that are important for object detection. The proposed
method enables bit rate savings of 7% or more compared to default HEVC, at the
equivalent object detection rate.Comment: The paper is published in IEEE ICASSP 18
DFacTo: Distributed Factorization of Tensors
We present a technique for significantly speeding up Alternating Least
Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor
factorization. By exploiting properties of the Khatri-Rao product, we show how
to efficiently address a computationally challenging sub-step of both
algorithms. Our algorithm, DFacTo, only requires two sparse matrix-vector
products and is easy to parallelize. DFacTo is not only scalable but also on
average 4 to 10 times faster than competing algorithms on a variety of
datasets. For instance, DFacTo only takes 480 seconds on 4 machines to perform
one iteration of the ALS algorithm and 1,143 seconds to perform one iteration
of the GD algorithm on a 6.5 million x 2.5 million x 1.5 million dimensional
tensor with 1.2 billion non-zero entries.Comment: Under review for NIPS 201
Mean values of Dirichlet polynomials and applications to linear equations with prime variables
We prove a new mean-value theorem for Dirichlet polynomials with coefficients
given by the von Mangoldt function. We then use our theorem to derive new
estimates for certain exponential sums over primes. The latter have
applications to additive problems with prime variables. In particular, we are
able to improve on a recent result of J.Y. Liu and K.M. Tsang on the size of
the solutions of linear equations with prime variables
Can you tell a face from a HEVC bitstream?
Image and video analytics are being increasingly used on a massive scale. Not
only is the amount of data growing, but the complexity of the data processing
pipelines is also increasing, thereby exacerbating the problem. It is becoming
increasingly important to save computational resources wherever possible. We
focus on one of the poster problems of visual analytics -- face detection --
and approach the issue of reducing the computation by asking: Is it possible to
detect a face without full image reconstruction from the High Efficiency Video
Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with
accuracy comparable to conventional face detection, by training a Convolutional
Neural Network on the output of the HEVC entropy decoder
Anomalous probability of large amplitudes in wave turbulence
Time evolution equation for the Probability Distribution Function (PDF) is
derived for system of weakly interacting waves. It is shown that a steady state
for such system may correspond to strong intermittency
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