285 research outputs found
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
We propose a new learning-based method for estimating 2D human pose from a
single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose
priors that are estimated from physiologically inspired graphical models or
learned from a holistic perspective. In this paper, we propose to integrate
both the local (body) part appearance and the holistic view of each local part
for more accurate human pose estimation. Specifically, the proposed DS-CNN
takes a set of image patches (category-independent object proposals for
training and multi-scale sliding windows for testing) as the input and then
learns the appearance of each local part by considering their holistic views in
the full body. Using DS-CNN, we achieve both joint detection, which determines
whether an image patch contains a body joint, and joint localization, which
finds the exact location of the joint in the image patch. Finally, we develop
an algorithm to combine these joint detection/localization results from all the
image patches for estimating the human pose. The experimental results show the
effectiveness of the proposed method by comparing to the state-of-the-art
human-pose estimation methods based on pose priors that are estimated from
physiologically inspired graphical models or learned from a holistic
perspective.Comment: CVPR 201
Co-interest Person Detection from Multiple Wearable Camera Videos
Wearable cameras, such as Google Glass and Go Pro, enable video data
collection over larger areas and from different views. In this paper, we tackle
a new problem of locating the co-interest person (CIP), i.e., the one who draws
attention from most camera wearers, from temporally synchronized videos taken
by multiple wearable cameras. Our basic idea is to exploit the motion patterns
of people and use them to correlate the persons across different videos,
instead of performing appearance-based matching as in traditional video
co-segmentation/localization. This way, we can identify CIP even if a group of
people with similar appearance are present in the view. More specifically, we
detect a set of persons on each frame as the candidates of the CIP and then
build a Conditional Random Field (CRF) model to select the one with consistent
motion patterns in different videos and high spacial-temporal consistency in
each video. We collect three sets of wearable-camera videos for testing the
proposed algorithm. All the involved people have similar appearances in the
collected videos and the experiments demonstrate the effectiveness of the
proposed algorithm.Comment: ICCV 201
Exploring Robust Features for Improving Adversarial Robustness
While deep neural networks (DNNs) have revolutionized many fields, their
fragility to carefully designed adversarial attacks impedes the usage of DNNs
in safety-critical applications. In this paper, we strive to explore the robust
features which are not affected by the adversarial perturbations, i.e.,
invariant to the clean image and its adversarial examples, to improve the
model's adversarial robustness. Specifically, we propose a feature
disentanglement model to segregate the robust features from non-robust features
and domain specific features. The extensive experiments on four widely used
datasets with different attacks demonstrate that robust features obtained from
our model improve the model's adversarial robustness compared to the
state-of-the-art approaches. Moreover, the trained domain discriminator is able
to identify the domain specific features from the clean images and adversarial
examples almost perfectly. This enables adversarial example detection without
incurring additional computational costs. With that, we can also specify
different classifiers for clean images and adversarial examples, thereby
avoiding any drop in clean image accuracy.Comment: 12 pages, 8 figure
Tuning the Magnetic Ordering Temperature of Hexagonal Ferrites by Structural Distortion Control
To tune the magnetic properties of hexagonal ferrites, a family of
magnetoelectric multiferroic materials, by atomic-scale structural engineering,
we studied the effect of structural distortion on the magnetic ordering
temperature (TN). Using the symmetry analysis, we show that unlike most
antiferromagnetic rare-earth transition-metal perovskites, a larger structural
distortion leads to a higher TN in hexagonal ferrites and manganites, because
the K3 structural distortion induces the three-dimensional magnetic ordering,
which is forbidden in the undistorted structure by symmetry. We also revealed a
near-linear relation between TN and the tolerance factor and a power-law
relation between TN and the K3 distortion amplitude. Following the analysis, a
record-high TN (185 K) among hexagonal ferrites was predicted in hexagonal
ScFeO3 and experimentally verified in epitaxially stabilized films. These
results add to the paradigm of spin-lattice coupling in antiferromagnetic
oxides and suggests further tunability of hexagonal ferrites if more lattice
distortion can be achieved
The spatial pattern and influencing factors of tourism eco-efficiency in Inner Mongolia, China
BackgroundTourism eco-efficiency is a performance basis for evaluating green total factor productivity and sustainable development.ObjectiveThe objective of this study was to measure tourism eco-efficiency in Inner Mongolia and explore its influencing factors. The aim was to provide an accurate reference for improving the quality and efficiency of tourism in Inner Mongolia and promoting the sustainable development of the regional economy and society.MethodsTourism eco-efficiency in Inner Mongolia from 2009 to 2019 was calculated using a super-slacks-based measure (SBM) model with an undesirable output. The spatial variation function was used to explore the spatial evolution pattern of tourism eco-efficiency in Inner Mongolia, and the influencing factors of the spatial evolution were analyzed by geographically weighted regression.ResultsTourism eco-efficiency in Inner Mongolia is relatively low. Eco-efficiency values among cities in Inner Mongolia vary, and their distribution is not balanced. The structural eco-efficiency of tourism in Inner Mongolia has been consistent from 2009 to 2019. The degree of homogenization in the overall direction is relatively good. Furthermore, its spatial distribution form and internal structure evolution show a certain regularity and continuity. The pattern evolution of tourism eco-efficiency in Inner Mongolia is jointly driven by the economic level, environmental regulation, industrial structure, traffic conditions, resource endowment, and tourism reception facilities. These influencing factors show obvious spatial heterogeneity.ConclusionFrom the perspective of Inner Mongolia, the difference in the tourism eco-efficiency value from 2009 to 2019 was relatively large, but the number of effective areas in the efficiency frontier generally showed a fluctuating growth trend. The range parameters of tourism eco-efficiency showed a decreasing trend, and the spatial correlation effect of tourism eco-efficiency in Inner Mongolia showed a decreasing trend under the influence of structural and spatial differentiation
Galaxy Deblending using Residual Dense Neural networks
We present a new neural network approach for deblending galaxy images in
astronomical data using Residual Dense Neural network (RDN) architecture. We
train the network on synthetic galaxy images similar to the typical
arrangements of field galaxies with a finite point spread function (PSF) and
realistic noise levels. The main novelty of our approach is the usage of two
distinct neural networks: i) a deblending network which isolates a single
galaxy postage stamp from the composite and, ii) a classifier network which
counts the remaining number of galaxies. The deblending proceeds by iteratively
peeling one galaxy at a time from the composite until the image contains no
further objects as determined by the classifier, or by other stopping criteria.
By looking at the consistency in the outputs of the two networks, we can assess
the quality of the deblending. We characterize the flux and shape
reconstructions in different quality bins and compare our deblender with the
industry standard, SExtractor. We also discuss possible future extensions for
the project with variable PSFs and noise levels.Comment: 15 pages, 13 figures, Accepted for publication in Physical Review
- …