检索结果-内蒙古大学图书馆

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Li, Jiangyuan Armandpour, Mohammadreza Texas A&M Univ Dept Stat College Stn TX 77843 USA

ISBN: (纸本)9781665405409

Wind power forecasting has drawn increasing attention among researchers as the consumption of renewable energy grows. In this paper, we develop a deep learning approach based on encoder-decoder structure. Our model forecasts wind power generated by a wind turbine using its spatial location relative to other turbines and historical wind speed data. In this way, we effectively integrate spatial dependency and temporal trends to make turbine-specific predictions. The advantages of our method over existing work can be summarized as 1) it directly predicts wind power based on historical wind speed, without the need for prediction of wind speed first, and then using a transformation;2) it can effectively capture long-term dependency 3) our model is more scalable and efficient compared with other deep learning based methods. We demonstrate the efficacy of our model on the benchmark real-world datasets.

关键词： Spatio-temporal model encoder-decoder wind power forecasting temporal relation

来源：评论

学校读者我要写书评

暂无评论

A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images

引用

SENSORS 2024年第22期24卷 7159页

作者： Wang, Xinhua Yuan, Botao Li, Zhuang Wang, Heqi Northeast Elect Power Univ Sch Comp Sci Jilin 132012 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China

The classification and recognition of features play a vital role in production and daily life;however, the current semantic segmentation of remote sensing images is hampered by background interference and other factors, leading to issues such as fuzzy boundary segmentation. To address these challenges, we propose a novel module for encoding and reconstructing multi-dimensional feature layers. Our approach first utilizes a bilinear interpolation method to downsample the multi-dimensional feature layer in the coding stage of the U-shaped framework. Subsequently, we incorporate a fractal curve module into the encoder, which aggregates points on feature maps from different layers, effectively grouping points from diverse regions. Finally, we introduce an aggregation layer that combines the upsampling method from the UNet series, employing the multi-scale censoring of multi-dimensional feature map outputs from various layers to efficiently capture both spatial and feature information. The experimental results across diverse scenarios demonstrate that our model achieves excellent performance in aggregating point information from feature maps, significantly enhancing semantic segmentation tasks.

关键词： remote sensing images bilinear interpolation fractal curve gather layers encoder-decoder semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Label-aware Attention Network with Multi-scale Boosting for Medical Image Segmentation

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Wang, Linbo Xu, Peng Cao, Xianfeng Nappi, Michele Wan, Shaohua Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc MOE Hefei 230601 Peoples R China Univ Salerno Dept Comp Sci Fisciano SA Italy Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen Peoples R China

Deep medical image segmentation calls for features with strong discrimination and rich scales due to ambiguous background distraction and large variations in object sizes and shapes. In this paper, we propose two modules to obtain these features. First, existing encoders tend to extract similar foreground/background features at blurry boundaries due to mixed-label feature aggregation. To enhance the discrimination of these features, a Label- Aware Attention (LAA) module is presented to reconstruct them by fusing same-label local features. The fusion is guided by local attention maps based on label-aware affinity learning. Second, instead of relying on a single encoder for scale context mining, we propose a Multi-scale Feature Boosting (MFB) module that applies parallel convolution with different receptive fields for scale embedding and integrates an additional backbone for cross- encoder scale reference. Combining LAA and MFB, a new encoder-decoder based framework is presented, where MFBs act as encoder blocks to recursively extract features with rich scale context, while LAA operates in the decoder layer to enhance the label-aware discriminativeness of features. Extensive experiments on three standard medical segmentation datasets demonstrate the effectiveness of the proposed framework.

关键词： Medical image segmentation Attention map encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting

引用

ENERGY CONVERSION AND MANAGEMENT 2024年 299卷

作者： Wu, Tangjie Ling, Qiang Univ Sci & Technol China Hefei 230027 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China

Wind energy has attracted more and more attention due to its sustainability and pollution-free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of great importance for fully harvesting wind energy and ensuring the stability and reliability of wind energy systems. By accurately predicting the future wind speed, we can make efficient power schedules and consequently improve the utilization of wind power. Although existing forecasting methods have achieved satisfactory wind forecasting performance, they may still fail to discover the intricate spatio-temporal dependencies obscured in entangled patterns. To resolve this issue and further improve the prediction accuracy, we propose Mixformer, a novel mixture Transformer with hierarchical context for spatio-temporal wind speed forecasting. Mixformer first applies seasonal-trend decomposition, and then employs an MLP to predict the trend part and an attention model to predict the seasonal part. Besides, Mixformer innovatively proposes a spatio-temporal Gaussian mixture attention (ST-GMA) layer, which fuses the periodic temporal and long-term spatial context. To capture intricate long-term spatial characteristics, Mixformer extracts global information by the Dynamic Time Warping algorithm. Moreover, with the ST-GMA layer, Mixformer builds a hierarchical encoder-decoder architecture to fully utilize the context at different scales for seasonal forecasting and further enhance the spatio-temporal modeling. Empirical experiments on four real-world benchmark datasets show that Mixformer yields the lowest prediction errors with the MAE scores of 1.66, 1.68, 3.14 and 0.70 and the RMSE scores of 2.07, 2.01, 3.99 and 0.98, and improves the prediction accuracy by 8.43%, 5.36%, 4.14%, 6.57% in term of MAE scores and 9.18%, 6.92%, 4.01%, 5.75% in term of RMSE scores compared to state-of-the-art methods, which demonstrates the superiority of Mixformer. In a nutshell, Mixformer presents a promising meth

关键词： Wind speed forecasting Spatio-temporal modeling Gaussian mixture attention encoder-decoder Seasonal-trend decomposition

来源：评论

学校读者我要写书评

暂无评论

SAA: SCALE-AWARE ATTENTION BLOCK FOR MULTI-LESION SEGMENTATION OF FUNDUS IMAGES 19

SAA: SCALE-AWARE ATTENTION BLOCK FOR MULTI-LESION SEGMENTATI...

引用

19th IEEE International Symposium on Biomedical Imaging (IEEE ISBI)

作者： Bo, Wang Li, Tao Liu, Xinhui Wang, Kai Nankai Univ Coll Comp Sci Tianjin Peoples R China

ISBN: (纸本)9781665429238

Multiple lesion segmentation, namely the segmentation of microaneurysms, soft exudate, hard exudate, and haemorrhage is very important to diabetic retinopathy diagnosis. However, the scales of different kinds of lesions are inconsistent. This inconsistent scale problem is unavoidable in the unified architecture design in which identical time of downsampling operations is used for different kinds of lesions. To achieve better performance at different scales, multiscale features need to be captured and adjusted. In this paper, we simply consider features from different stages of an encoder-decoder network as multiscale features. To re-weight importance of multiscale features dynamically, a scale-aware attention (SAA) block which consists of a spatial path and a channel path is introduced. In SAA block, adjusting operations are performed scale-wise instead of channel-wise or uniformly for all scales. Extensive experiments were conducted on two publicly-available datasets to verify the effect of SAA. SAA surpasses popular attention blocks and state-of-the-art results in the overall evaluation while comparable performance can be achieved in the individual evaluation at the same time.

关键词： Lesion segmentation inconsistent scale encoder-decoder attention block

来源：评论

学校读者我要写书评

暂无评论

Study of facial generation methods after orthodontic treatment 46

Study of facial generation methods after orthodontic treatme...

引用

46th Annual IEEE-Computer-Society International Computers, Software, and Applications Conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World

作者： Tian, Jia-Liang Zhang, Qin-Yan Li, Hai-Zhen Wang, Qing Lei, Yi Zang, Lin Gao, Xue-Mei Yang, Ji-Jiang Beijing Univ Posts & Telecommun Coll Artificial Intelligence Beijing Peoples R China Peking Univ Dept Orthodont Sch & Hosp Stomatol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Sch Software Engn Beijing Peoples R China Pharmacovigilance Res Ctr Informat Technol & Data Xiamen Fujian Peoples R China

ISBN: (纸本)9781665488105

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on encoder-decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.

关键词： Face Generation Orthodontic Treatment StyleGAN encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

A COMBINED FEATURE ENCODING NETWORK WITH SEMANTIC ENHANCEMENT FOR IMAGE TAMPERING FORENSICS 18th

A COMBINED FEATURE ENCODING NETWORK WITH SEMANTIC ENHANCEMEN...

引用

18th IFIP WG 11.9 International Conference on Digital Forensics

作者： Luo, Yuling Liang, Ce Zhang, Shunsheng Qin, Sheng Guangxi Normal Univ Elect Engn Guilin Peoples R China Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China

ISBN: (纸本)9783031100789;9783031100772

Image tampering forensics is performed by analyzing images to locate the tampered regions. However, most image tampering detection methods lack locational accuracy and are effective only for specific types of tampering. To address these problems, this chapter proposes a method that employs an encoder-decoder network structure with combined multiple feature encoding to segment tampered regions of an image from untampered regions. Three features, obtained using constrained convolution, steganalysis rich model filtering and common convolution, are combined. During the encoding stage, ring residual units are used to extract features. The combination of multiple features and the ring residual units makes the proposed method most suitable for image tampering detection. Channel attention with a soft threshold function is used to reinforce semantic information in the decoding stage. Experiments with three image forensic datasets, NIST16, COVERAGE and CASIA, demonstrate that the proposed method exhibits strong performance in terms of the F1 score and localization of tampered regions.

关键词： Image tampering encoder-decoder combined features semantics

来源：评论

学校读者我要写书评

暂无评论

Fast pixel-matching for video object segmentation

引用

SIGNAL PROCESSING-IMAGE COMMUNICATION 2021年 98卷 116373-116373页

作者： Yu, Siyue Xiao, Jimin Zhang, Bingfeng Lim, Eng Gee Zhao, Yao Xian Jiaotong Liverpool Univ Suzhou Jiangsu Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

Video object segmentation, aiming to segment the foreground objects given the annotation of the first frame, has been attracting increasing attentions. Many state-of-the-art approaches have achieved great performance by relying on online model updating or mask-propagation techniques. However, most online models require high computational cost due to model fine-tuning during inference. Most mask-propagation based models are faster but with relatively low performance due to failure to adapt to object appearance variation. In this paper, we are aiming to design a new model to make a good balance between speed and performance. We propose a model, called NPMCA-net, which directly localizes foreground objects based on mask-propagation and non-local technique by matching pixels in reference and target frames. Since we bring in information of both first and previous frames, our network is robust to large object appearance variation, and can better adapt to occlusions. Extensive experiments show that our approach can achieve a new state-of-the-art performance with a fast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on DAVIS-2017, with speed of 0.11s per frame) under the same level comparison. Source code is available at https://***/siyueyu/NPMCA-net.

关键词： Non-local pixel matching Mask-propagation encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Automatic Report Generation for Chest X-Ray Images via Adversarial Reinforcement Learning

引用

IEEE ACCESS 2021年 9卷 21236-21250页

作者： Hou, Daibing Zhao, Zijian Liu, Yuying Chang, Faliang Hu, Sanyuan Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong First Med Univ Affiliated Hosp 1 Dept Gen Surg Jinan 250014 Peoples R China

An adversarial reinforced report-generation framework for chest x-ray images is proposed. Previous medical-report-generation models are mostly trained by minimizing the cross-entropy loss or further optimizing the common image-captioning metrics, such as CIDEr, ignoring diagnostic accuracy, which should be the first consideration in this area. Inspired by the generative adversarial network, an adversarial reinforcement learning approach is proposed for report generation of chest x-ray images considering both diagnostic accuracy and language fluency. Specifically, an accuracy discriminator (AD) and fluency discriminator (FD) are built that serve as the evaluators by which a report based on these two aspects is scored. The FD checks how likely a report originates from a human expert, while the AD determines how much a report covers the key chest observations. The weighted score is viewed as a "reward" used for training the report generator via reinforcement learning, which solves the problem that the gradient cannot be passed back to the generative model when the output is discrete. Simultaneously, these two discriminators are optimized by maximum-likelihood estimation for better assessment ability. Additionally, a multi-type medical concept fused encoder followed by a hierarchical decoder is adopted as the report generator. Experiments on two large radiograph datasets demonstrate that the proposed model outperforms all methods to which it is compared.

关键词： Medical diagnostic imaging Decoding Training Generators Semantics Feature extraction X-ray imaging Medical report generation encoder-decoder adversarial training reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

引用

IEEE ACCESS 2021年 9卷 83002-83024页

作者： Khan, Muhammad Zubair Gajendran, Mohan Kumar Lee, Yugyung Khan, Muazzam A. Univ Missouri Sch Comp & Engn Kansas City MO 64110 USA Quaid I Azam Univ Dept Comp Sci Islamabad 44000 Pakistan

Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

关键词： Image segmentation Medical diagnostic imaging Computer architecture Semantics Convolution Recurrent neural networks Deep learning Deep learning convolution neural network medical image analysis semantic segmentation skip-connections encoder-decoder computer-aided diagnostics healthcare

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：