检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Li, Haoqing Yang, Jinfu Wang, Runshi Xu, Yifei The Faculty of Information Beijing University of Technology Beijing100124 China The Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN’s representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22%nIoU and 1.33×10−6Fa) and IRSTD-1K (68.91%nIoU and 3.23×10−6Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://***/Li-Haoqing/ILNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

CGNet: Classification-Guided Multi-Task Interactive Network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion Information exchange Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI-SCALE AGGREGATION AND BOUNDARY-AWARE FOR LATERAL VENTRICLE SEGMENTATION ON MR IMAGES 47

A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI...

引用

2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022

作者： Ye, Fei Wang, Zhiqiang Zhu, Sheng Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Department of Radiology Affiliated Hospital of Xiangnan University Chenzhou423000 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781665405409

In this paper, we propose a novel convolutional neural network based on adaptive multi-scale feature aggregation and boundary-aware for lateral ventricle segmentation (MB-Net), which mainly includes three parts, i.e., an adaptive multi-scale feature aggregation module (AMSFM), an embedded boundary refinement module (EBRM), and a local feature extraction module (LFM). Specifically, the AMSFM is used to extract multi-scale features through the different receptive fields to effectively solve the problem of distinct target regions on magnetic resonance (MR) images. The EBRM is intended to extract boundary information to effectively solve blurred boundary problems. The LFM can make the extraction of local information based on spatial and channel attention mechanisms to solve the problem of irregular shapes. Finally, extensive experiments are conducted from different perspectives to evaluate the performance of the proposed MB-Net. Furthermore, we also verify the robustness of the model on other public datasets, i.e., COVID-SemiSeg and CHASE DB1. The results show that our MB-Net can achieve competitive results when compared with state-of-the-art methods. © 2022 IEEE

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

Deep Spatial and Temporal Information based QoE Evaluation Model for HTTP Adaptive Streaming 2021

Deep Spatial and Temporal Information based QoE Evaluation M...

引用

2021 5th International Conference on Computer Science and Artificial intelligence

作者： Lina Du Li Zhuo Jiafeng Li Hui Zhang Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology China and Faculty of Information Technology Beijing University of Technology China

ISBN: (纸本)9781450384155

The content characteristics of video is one of the important influencing factors affecting the user's Quality of Experience (QoE). In this paper, deep spatial and temporal information are extracted to characterize the content characteristics of video, which are then used to establish a QoE evaluation model for HTTP adaptive streaming. Firstly, a Gabor convolutional layer and Channel Attention (CA) are incorporated into ResNet18 to construct the Gabor-CA-ResNet18 network, which is used to capture the Deep Spatial Information (DSI) of video. To avoid the problem of the "curse of dimensionality", LargeVis is applied to reduce the dimensionality of the DSI features to improve the representative and discriminative ability, obtaining a compact feature representation vector. Secondly, 3D Convolutional Neural Networks (3D CNN) and Gated Recurrent Unit (GRU) are used together to capture the Deep Temporal Information (DTI) of video, named 3D CNN-GRU. And finally, the DSI and DTI features are combined with the statistical features of other influencing factors, including video quality level, re-buffering duration, re-buffering frequency, and so on, to form the feature parameter vector. The Gradient Boosting method is adopted to establish the mapping relationship model between the feature parameter vector and Mean Opinion Score (MOS), which can be used to predict the user's QoE. Experimental results on SQoE-III and SQoE-IV datasets demonstrate that the proposed QoE model can achieve the state-of-the-art performance compared with the existing QoE evaluation models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Continual Learning Methods for Object Hardness Recognition

Evaluation of Continual Learning Methods for Object Hardness...

引用

2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Liu, Yiwen Fang, Senlin Liu, Chengliang Wang, Jingnan Mai, Ke Zhang, Yupo Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China City University of Macau Faculty of Data Science 999078 China

ISBN: (纸本)9798350327182

How to solve the problem of continual learning of robotic tactile perception in an open, dynamic environment is a pressing task. Although continual learning has been extensively studied in computer vision, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of tactile robotics. In this paper, we propose a continual learning scenario in the robotic tactile domain, and design experiments with tactile hardness dataset that allow us to compare the performance of many continual learning algorithms in the tactile domain. We examine the performance of regularization-based and replay methods in the tactile domain and discuss the effect of replay number on model performance. We show that regularization-based methods do not perform well on the tactile dataset. On the contrary, replay methods can maintain better anti-forgetting performance, but the number of model parameters will be larger, and the training time will be longer. We also found that the replay ratio should be higher than 0.2 to maintain the performance of the replay method on the tactile hardness dataset. © 2023 IEEE.

关键词： Robotics

来源：评论

学校读者我要写书评

暂无评论

Research on a panoramic video transmission scheme based on multi-view switching within the human eye's field of view

Research on a panoramic video transmission scheme based on m...

引用

Chinese Control and Decision Conference, CCDC

作者： Jiangeng Li Kai Xu Guangsheng Wang College of Automation Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781665478977

In today's Internet era, people have increasingly higher requirements for video clarity and breadth of vision. The scene information that can be expressed by traditional video is limited, and it is difficult to meet people's needs anymore, and panoramic video has emerged as the times require. Panoramic video can bring people an immersive and interactive audio-visual experience. However, due to its own characteristics, panoramic video with ultra-high resolution and ultra-high bit rate has a high demand for network bandwidth during network transmission. This paper compares the existing panoramic video transmission schemes, analyzes the advantages and disadvantages of each scheme, and proposes a transmission scheme based on multi-view switching within the human eye’s field of view, and then verifies the effectiveness of the scheme in saving network bandwidth by building a live platform.

关键词： Costs Splicing Buildings Bit rate Switches Bandwidth Control systems

来源：评论

学校读者我要写书评

暂无评论

Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites

Real-time In-situ Defect Detection in 3D Printing of Continu...

引用

Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Zhijian Yue Zhen Shen Xiangyang Dong Qihang Fang Weixing Wang Xisong Dong Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach.

关键词： Training Solid modeling Accuracy Attention mechanisms Training data Three-dimensional printing Real-time systems Quality assessment Optical fiber testing Defect detection

来源：评论

学校读者我要写书评

暂无评论

Heavy-Ball Momentum Accelerated Actor-Critic With Function Approximation

arXiv

引用

arXiv 2024年

作者： Dong, Yanjie Zhang, Haijun Wang, Gang Cui, Shisheng Hu, Xiping The Artificial Intelligence Research Institute Guangdong-Hong Kong Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen518172 China The Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing China The State Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

By using an parametric value function to replace the Monte-Carlo rollouts for value estimation, the actor-critic (AC) algorithms can reduce the variance of stochastic policy gradient so that to improve the convergence rate. While existing works mainly focus on analyzing convergence rate of AC algorithms under Markovian noise, the impacts of momentum on AC algorithms remain largely unexplored. In this work, we first propose a heavy-ball momentum based advantage actor-critic (HB-A2C) algorithm by integrating the heavy-ball momentum into the critic recursion that is parameterized by a linear function. When the sample trajectory follows a Markov decision process, we quantitatively certify the acceleration capability of the proposed HB-A2C algorithm. Our theoretical results demonstrate that the proposed HB-A2C finds an ǫ-approximate stationary point with O(ǫ−2) iterations for reinforcement learning tasks with Markovian noise. Moreover, we also reveal the dependence of learning rates on the length of the sample trajectory. By carefully selecting the momentum factor of the critic recursion, the proposed HB-A2C can balance the errors introduced by the initialization and the stoschastic approximation. © 2024, CC BY.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

system for the large scale intelligent treatment of sludge 2

System for the large scale intelligent treatment of sludge

引用

2nd International Conference on Information Technology and intelligent Control, CITIC 2022

作者： Yang, Rui Gan, Lin Mo, Qinglong Li, Yuhong Hu, Ruihan Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing Guangzhou China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery Shihezi University Shihezi China

ISBN: (纸本)9781510657649

In this study, by examining the effects of the optimal carbon-nitrogen ratio and other factors on the composting effect, the optimal composting parameters of municipal sludge and kitchen waste were determined, and the carbon emissions were calculated at the same time, It had reference value for carbon neutralization and carbon peak. On the basis of reduction, harmlessness and resource utilization, a coordinated aerobic composting process for municipal sludge, kitchen waste and agricultural and forestry waste was established. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Carbon

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：