744 research outputs found
Pairwise Teacher-Student Network for Semi-Supervised Hashing
Hashing method maps similar high-dimensional data to binary hashcodes with
smaller hamming distance, and it has received broad attention due to its low
storage cost and fast retrieval speed. Pairwise similarity is easily obtained
and widely used for retrieval, and most supervised hashing algorithms are
carefully designed for the pairwise supervisions. As labeling all data pairs is
difficult, semi-supervised hashing is proposed which aims at learning efficient
codes with limited labeled pairs and abundant unlabeled ones. Existing methods
build graphs to capture the structure of dataset, but they are not working well
for complex data as the graph is built based on the data representations and
determining the representations of complex data is difficult. In this paper, we
propose a novel teacher-student semi-supervised hashing framework in which the
student is trained with the pairwise information produced by the teacher
network. The network follows the smoothness assumption, which achieves
consistent distances for similar data pairs so that the retrieval results are
similar for neighborhood queries. Experiments on large-scale datasets show that
the proposed method reaches impressive gain over the supervised baselines and
is superior to state-of-the-art semi-supervised hashing methods
Single-Shot Refinement Neural Network for Object Detection
For object detection, the two-stage approach (e.g., Faster R-CNN) has been
achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has
the advantage of high efficiency. To inherit the merits of both while
overcoming their disadvantages, in this paper, we propose a novel single-shot
based detector, called RefineDet, that achieves better accuracy than two-stage
methods and maintains comparable efficiency of one-stage methods. RefineDet
consists of two inter-connected modules, namely, the anchor refinement module
and the object detection module. Specifically, the former aims to (1) filter
out negative anchors to reduce search space for the classifier, and (2)
coarsely adjust the locations and sizes of anchors to provide better
initialization for the subsequent regressor. The latter module takes the
refined anchors as the input from the former to further improve the regression
and predict multi-class label. Meanwhile, we design a transfer connection block
to transfer the features in the anchor refinement module to predict locations,
sizes and class labels of objects in the object detection module. The
multi-task loss function enables us to train the whole network in an end-to-end
way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO
demonstrate that RefineDet achieves state-of-the-art detection accuracy with
high efficiency. Code is available at https://github.com/sfzhang15/RefineDetComment: 14 pages, 7 figures, 7 table
SFD: Single Shot Scale-invariant Face Detector
This paper presents a real-time face detector, named Single Shot
Scale-invariant Face Detector (SFD), which performs superiorly on various
scales of faces with a single deep neural network, especially for small faces.
Specifically, we try to solve the common problem that anchor-based detectors
deteriorate dramatically as the objects become smaller. We make contributions
in the following three aspects: 1) proposing a scale-equitable face detection
framework to handle different scales of faces well. We tile anchors on a wide
range of layers to ensure that all scales of faces have enough features for
detection. Besides, we design anchor scales based on the effective receptive
field and a proposed equal proportion interval principle; 2) improving the
recall rate of small faces by a scale compensation anchor matching strategy; 3)
reducing the false positive rate of small faces via a max-out background label.
As a consequence, our method achieves state-of-the-art detection performance on
all the common face detection benchmarks, including the AFW, PASCAL face, FDDB
and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for
VGA-resolution images.Comment: Accepted by ICCV 2017 + its supplementary materials; Updated the
latest results on WIDER FAC
Numerical Analysis of Computing Quasiperiodic Systems
Quasiperiodic systems, related to irrational numbers, are important
space-filling ordered structures, without decay and translational invariance.
There are some efficient numerical algorithms, such as the projection method
(PM) [J. Comput. Phys., 256: 428, 2014], have been proposed to compute
quasiperiodic systems. However, there is also a lack of theoretical analysis of
these numerical methods. In this paper, we first establish a mathematical
framework for the quasiperiodic function and its high-dimensional periodic
function based on Birkhoff's ergodic theorem. Then we give a theoretical
analysis of PM and quasiperiodic spectral method (QSM). Results demonstrate
that PM and QSM both have exponential decay. Further, we find that QSM (PM) is
a generalization of the conventional Fourier (pseudo) spectral method. And the
PM can use fast Fourier transform to treat nonlinear problems and cross terms
with an almost optimal computational amount. Finally, we use the quasiperiodic
Schr\"odinger equation as an example to verify our theoretical results
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