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