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Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of deep neural networks, researches on point cloud completion have made ...

Class activation maps generated by image classifiers are widely used as priors for image-level weakly supervised semantic segmentation. However, these activation maps mainly focus on the sparse discriminative regions, which has been a bottleneck for the ...

One-stage multi-object tracking methods have achieved promising results by showing their great balance between accuracy and speed. However, the internal differences and relationships between detection and re-identification (re-ID) lead to worse ...

Video salient object detection (VSOD) aims at distinguishing the salient objects from the complex background and highlighting them uniformly in the spatiotemporal domain. One of the fundamental challenges in VSOD is how to make the most use of the ...

With the progress of communication technology, the increasing wireless devices are hooked up the networks, and the consequent security problems cannot be ignored. At present, the solutions mainly include secret key system and physical layer ...

The single‐input multiple‐out (SIMO) system considered is the Wyner eavesdropper model with decoding and forwarding (DF) relay over Beaulieu‐Xie fading. The two performance evaluation indicators of secure outage probability (SOP) and strictly positive ...

In order to improve the estimation accuracy of structural change points of multi-dimensional stochastic model, the accurate estimation algorithm of structural change points of multi-dimensional stochastic model is studied. A multi-dimensional stochastic ...

Objective. To explore the clinical effect of acupuncture combined with traditional Chinese medicine (TCM) application on the treatment of functional dyspepsia (FD) in children and the influence on serum 5-HT and NO levels. Methods. 94 FD children admitted ...

Most of learning targets for multi-person pose estimation are based on the likelihood $P(Y|X)$. However, if we construct the causal assumption for keypoints, named a Structure Causal Model (SCM) for the causality, $P(Y|X)$ will introduce the bias via ...$$$$

Recently, most existing human pose estimation methods fuse multi-stage convolutional modules to learn a shared feature representation. In this paper, we propose a expectation–maximization (EM) mapping-based network to learn specific related body ...

With the development of Internet of Things (IoTs), devices are now connecting and communicating together on a heretofore unheard-of scale, forming huge heterogeneous networks of mobile IoT-enabled devices. For beyond 5G- (B5G-) enabled networks, this ...

This paper addresses the core issue of how to learn powerful features for saliency. We have two major observations. First, feature maps of different layers in convolutional neural networks play different roles in saliency detection. Second, different ...

Recently, benefiting from the fast development of deep convolutional neural networks, salient object detection (SOD) has achieved gratifying performance in a variety of challenging scenarios. Among them, how to learn more discriminative features ...

Blind motion deblurring is one of the most basic and challenging problems in image processing and computer vision. It aims to recover a sharp image from its blurred version knowing nothing about the blurring process. Many existing methods use the ...

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the ...

This paper presents a novel and effective architecture for scene semantic segmentation, named Local Context Embedding (LCE) network. Unlike previous work, in this paper we characterize local context by exploiting the content of image patches to ...

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• Focusing on the characteristics of ICU data, machine learning methods are used to realize the mortality prediction.

With the development of biomedical equipment and healthcare level, large amounts of data have been brought out in hospital, especially in Intensive Care Unit (ICU). However, how to better exploit meaningful information from these rich ...

Category-specific object segmentation has been a long-standing research topic in pattern recognition. This paper presents an unsupervised discriminant shape (UDS) to address category-specific object segmentation by incorporating the proposed shape prior ...

In this paper, we address the problem of fusing various saliency detection methods such that the fusion result outperforms each of the individual methods. We observe that the saliency regions shown in different saliency maps are with high probability ...

In current broadband system schemes, such as VSF-OFCDM, TDD-MIMO-OFDM, TDD-CDM-OFDM and so on, CDMA or OFDMA was adopted as multi-access schemes. The channel estimation overhead and multiuser detection complexity, were the main factors that limited ...

Extracting objects from natural images has long been an active problem in image processing. Despite various attempts, it has not been completely solved up to date. Current state-of-the-art object proposal methods tend to extract a set of object segments ...