Joint Object and Part Segmentation using Deep Learned Potentials

Segmenting semantic objects from images and parsing them into their respective semantic parts are fundamental steps towards detailed object understanding in computer vision. In this paper, we propose a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part segmentation, and more detailed part-level localization is utilized to refine object segmentation. Specifically, we first introduce the concept of semantic compositional parts (SCP) in which similar semantic parts are grouped and shared among different objects. A two-channel fully convolutional network (FCN) is then trained to provide the SCP and object potentials at each pixel. At the same time, a compact set of segments can also be obtained from the SCP predictions of the network. Given the potentials and the generated segments, in order to explore long-range context, we finally construct an efficient fully connected conditional random field (FCRF) to jointly predict the final object and part labels. Extensive evaluation on three different datasets shows that our approach can mutually enhance the performance of object and part segmentation, and outperforms the current state-of-the-art on both tasks

Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

With the development of economic globalization and modern information and communication technology, the situation of communication fraud is becoming more and more serious. How to identify fraudulent calls accurately and effectively has become an urgent task in current telecommunications operations. Affected by the sample set and the current state of the art, the current machine learning methods used to identify the imbalanced distribution dataset of positive and negative samples have low recognition accuracy. Therefore, in this paper, we propose a new hybrid model solution that uses feature construction, feature selection and imbalanced classes handling. A stacking model fusion algorithm composed of a two-layer stacking framework with several state-of-the-art machine learning classifiers is adopted. The results show that the risk user identification model based on mobile network communication behavior established by our stacking model fusion algorithm can accurately predict the category labels of telecom users and improve the risk of telecom users. The generalization performance of the identification is high, which provides a certain reference for the telecommunications industry to identify risk users based on mobile network communication behaviors

Effect of architectural adjustments on pedestrian flow at bottleneck

In the last decades, a series of terrible accidents happened within pedestrian crowds, which makes crowd dynamic a significant issue to be investigated. Literature reviews show that pedestrian flow presents different features within different architectural layout. In this paper, pedestrian movement properties at bottleneck are studied by carrying out series of experiments under laboratory condition. The influence of door sizes and exit locations on pedestrian crowd flow is investigated. It was found that larger door width resulted in shorter evacuation time and faster flow rate. By comparing the fundamental diagram among crowd evacuation, the average velocity increases as the width increases under the same density condition. Interestingly, the influence of the boundary layer, as well as the effective width on pedestrian crowd dynamic, was clearly observed. Our results suggest that the combination of exit width and location resulted in a synergistic effect, but the exit widths gradually became the most important factor influencing the flow rate

Mid-infrared variability of changing-look AGN

It is known that some active galactic nuclei (AGNs) transited from type 1 to type 2 or vice versa. There are two explanations for the so-called changing look AGNs: one is the dramatic change of the obscuration along the line-of-sight, the other is the variation of accretion rate. In this paper, we report the detection of large amplitude variations in the mid-infrared luminosity during the transitions in 10 changing look AGNs using WISE and newly released NEOWISE-R data. The mid-infrared light curves of 10 objects echoes the variability in the optical band with a time lag expected for dust reprocessing. The large variability amplitude is inconsistent with the scenario of varying obscuration, rather supports the scheme of dramatic change in the accretion rate.Comment: Published by ApjL, 7 pages, 3 figures, 2 table