检索结果-内蒙古大学图书馆

19th International Symposium on Visual Computing

作者： Hiraman, Anura Viriri, Serestina Gwetu, Mandlenkosi Univ KwaZulu Natal Sch Math Stat & Comp Sci Durban South Africa

ISBN: (纸本)9783031773914;9783031773921

Over the past decade, deep learning has significantly impacted medical imaging, particularly in segmentation tasks. encoder-decoder methods have advanced medical image segmentation by restoring feature map resolution and minimizing information loss during decoding. This paper introduces three key contributions: a method for region of interest (RoI) detection that isolates the lung region by classifying CT scan slices using two convolutional neural networks-one for slices above the lungs;an architecture inspired by U-Net, which employs a convolutional encoder-decoder-encoder-decoder pattern to enhance learning by feeding output feature maps from the first decoder into the second encoder;and concatenation layers between the encoder-decoder networks that reintroduce important features lost in the initial structure, improving the learning of complex features. Evaluation results demonstrate the effectiveness of these methods for lung segmentation, achieving an average DSC of 92.68%.

关键词： CNN encoder-decoder Lung segmentation Region of interest

来源：评论

学校读者我要写书评

暂无评论

A stroke of genius: Predicting the next move in badminton

A stroke of genius: Predicting the next move in badminton

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ibh, Magnus Grasshof, Stella Hansen, Dan Witzner IT Univ Copenhagen Machine Learning Grp Copenhagen Denmark

ISBN: (纸本)9798350365474

This paper presents, RallyTemPose, a transformer encoder-decoder model for predicting future badminton strokes based on previous rally actions. The model uses court position, skeleton poses, and player-specific embeddings to learn stroke and player-specific latent representations in a spatiotemporal encoder module. The representations are then used to condition the subsequent strokes in a decoder module through rally-aware fusion blocks, which provide additional relevant strategic and technical considerations to make more informed predictions. RallyTemPose shows improved forecasting accuracy compared to traditional sequential methods on two real-world badminton datasets. The performance boost can also be attributed to the inclusion of improved stroke embeddings extracted from the latent representation of a pre-trained large-language model subjected to detailed text descriptions of stroke descriptions. In the discussion, the latent representations learned by the encoder module show useful properties regarding player analysis and comparisons. The code can be found at: This https url.

关键词： Action Forecasting Computer Vision encoder-decoder Skeleton-data Sports Application

来源：评论

学校读者我要写书评

暂无评论

Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems

引用

IEEE WIRELESS COMMUNICATIONS LETTERS 2022年第4期11卷 846-850页

作者： Xie, Wenwu Xiao, Jian Zhu, Peng Yu, Chao Yang, Liang Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Adv Cryptog & Syst Secur Key Lab Sichuan Prov Chengdu 610103 Peoples R China

To reduce the pilot overhead of cascaded channel estimation for RIS-aided Massive MIMO communication system, we proposed a deep compressed sensing-based channel estimation scheme, where U-shaped network (U-Net), an encoder-decoder with skip connection, is used to recover the high-dimensional cascaded channel matrix from limited pilot overhead. The skip connections between encoder and decoder can fuse features of different scales and semantic by concatenating the feature map, which enhance the reconstruction performance of cascaded channel. To further improve the feature extraction ability of U-Net, we design a ResU-Net architecture with stacked residual units to increase the depth of network. Simulation results show the channel estimation of ResU-Net is more accurate than conventional algorithm and other network model. Meanwhile, ResU-Net has good generalization and robustness for different pilot lengths and phase quantization errors.

关键词： Channel estimation Azimuth Quantization (signal) Estimation Sensors Compressed sensing Chaos Reconfigurable intelligent surface channel estimation deep compressed sensing encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Improving offline handwritten Chinese text recognition with glyph-semanteme fusion embedding

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第2期13卷 485-496页

作者： Zhan, Hongjian Lyu, Shujing Lu, Yue Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai 200062 Peoples R China

In this paper, we propose the Glyph-Semanteme fusion Embedding (GSE) for Chinese character and apply it to Offline Handwritten Chinese Text Recognition (offline-HCTR). It is well known that the number of Chinese characters is very large and the glyphs of these characters are complex, but few researchers realize that the underlying reason for this phenomenon is that Chinese is a form of ideogram, which indicates that there are correlations between the glyph and semanteme of a character. In order to utilize this feature and create better representations for Chinese characters, firstly, we extract the glyph embedding and semanteme embedding for each Chinese character;then we propose a parameterized gated fusion strategy to automatically calculate the Glyph-Semanteme fusion Embedding for each character by fusing its glyph embedding and semanteme embedding. We apply the proposed GSE to an attention-based encoder-decoder network for the offline-HCTR task. Furthermore, two kinds of GSE, Character-level GSE (CGSE) and Text-level GSE (TGSE), are applied to the decoder phase to yield the predictions. On the standard benchmark ICDAR-2013 HCTR competition dataset, the proposed method achieves 96.65% character-level recognition accuracy, which demonstrates the effectiveness of the proposed glyph-semanteme fusion embedding.

关键词： Offline Handwritten Chinese text recognition Glyph embedding Semanteme embedding Embedding fusion encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Intelligent machinery health prognostics under variable operation conditions with limited and variable-length data

引用

ADVANCED ENGINEERING INFORMATICS 2022年第0期53卷

作者： Ding, Peng Jia, Minping Ding, Yifei Cao, Yudong Zhao, Xiaoli Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

High accuracy health prognostics are significant to machinery intelligent operation and maintenance. Current data-driven prognostics achieve great success that benefits from amply learning samples. In fact, data scarcity challenge widely exists in machinery prognostics and health management, especially for high-end equipment. This study aims to solve this dilemma and proposes a novel meta learning algorithm reconstructed by classic variable-length prediction mode and attention mechanism, namely meta attention recurrent neural network (MARNN). Specifically, we first develop the encoder-decoder with attention mechanism (EDA) cell to perform episodic learning for the subtask-level upgrade. Then multiple subtasks with EDA as prediction models are aggregated to accomplish meta-level upgrade, thus mining the general degradation knowledge from historical datasets. Finally, cross-domain prognostics tasks can be easily realized through fine-tuning tricks, and three rotating machinery run-to-failed experiments are conducted to prove the generalizations of MARNN, which can obtain desired results even when the on-site adaptation data is reduced to one-twentieth.

关键词： Limiteddatadrivenhealthprognostics encoder-decoder Attentionmechanism Metalearning Rotatingmachinery

来源：评论

学校读者我要写书评

暂无评论

Multilevel Attention Based U-Shape Graph Neural Network for Point Clouds Learning

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第1期18卷 448-456页

作者： Zou, Xiaofeng Li, Kenli Chen, Cen Hunan Univ Coll Informat Sci & Engn Changsha 410082 Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Peoples R China Infocomm Res Inst I2R Singapore 126777 Singapore

With the popularity of 3-D sensors in industrial Internet of Things (IIoT), point clouds learning is increasingly important. In this article, we propose a novel multilevel attention based U-shape graph neural network (MAUGNN) for point clouds learning, which can effectively learn the features from low-level to high-level and fuse multiple-level features based on the graph neural networks and attention mechanism. There are three parts in MAUGNN: encoder, decoder, and connections. In the encoder and decoder, we design an attention-based graph convolution to explore the structural information for point clouds. During the encoder, a structure-aware attention pooling is proposed to support down-sampling on point cloud data. To adaptively fuse coarse-grained features from the encoder and fine-grained features from the decoder together, we also propose a structure-aware attention skip connection mechanism. Extensive experiments on popular point cloud datasets demonstrate the superior performance of our MAUGNN over state-of-the-art baselines.

关键词： Three-dimensional displays Decoding Fuses Task analysis Feature extraction Encoding Semantics Attention mechanism encoder-decoder graph neural networks (GNNs) point clouds

来源：评论

学校读者我要写书评

暂无评论

A review of sign language recognition research

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 3879-3898页

作者： Yu, Ming Jia, Jingli Xue, Cuihong Yan, Gang Guo, Yingchun Liu, Yuehao Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Tianjin Univ Technol Tech Coll Deaf Tianjin Peoples R China

Sign language is the primary way of communication between hard-of-hearing and hearing people. Sign language recognition helps promote the better integration of deaf and hard-of-hearing people into society. We reviewed 95 types of research on sign language recognition technology from 1993 to 2021, analyzing and comparing algorithms from three aspects of gesture, isolated word, and continuous sentence recognition, elaborating the evolution of sign language acquisition equipment and we summarized the datasets of sign language recognition research and evaluation criteria. Finally, the main technology trends are discussed, and future challenges are analyzed.

关键词： Sign language recognition convolutional neural network encoder-decoder dataset

来源：评论

学校读者我要写书评

暂无评论

FRDD-Net: Automated Carotid Plaque Ultrasound Images Segmentation Using Feature Remapping and Dense Decoding

引用

SENSORS 2022年第3期22卷 887页

作者： Li, Yanhan Zou, Lian Xiong, Li Yu, Fen Jiang, Hao Fan, Cien Cheng, Mofan Li, Qi Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Wuhan Univ Zhongnan Hosp Cardiovasc Ultrasound Dept Wuhan 430071 Peoples R China Huazhong Univ Sci & Technol Cent Hosp Wuhan Tongji Med Coll Dept Ultrasound Wuhan 430014 Peoples R China

Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound images and heterogenous characteristics of carotid plaques. To address those problems, in this paper, we propose a novel deep convolutional neural network, FRDD-Net, with an encoder-decoder architecture to automatically segment carotid plaques. We propose the feature remapping modules (FRMs) and incorporate them into the encoding and decoding blocks to ameliorate the reliability of acquired features. We also propose a new dense decoding mechanism as part of the decoder, thus promoting the utilization efficiency of encoded features. Additionally, we construct a compound loss function to train our network to further enhance its robustness in the face of numerous cases. We train and test our network in multiple carotid plaque ultrasound datasets and our method yields the best performance compared to other state-of-the-art methods. Further ablation studies consistently show the advancement of our proposed architecture.

关键词： ultrasound segmentation deep convolutional neural networks carotid plaques encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Combine unlabeled with labeled MR images to measure acute ischemic stroke lesion by stepwise learning

引用

IET IMAGE PROCESSING 2022年第14期16卷 3965-3976页

作者： Zhao, Bin Liu, Zhiyang Liu, Guohua Wu, Mengran Cao, Chen Jin, Song Wu, Hong Ding, Shuxue Nankai Univ Tianjin Key Lab Optoelect Sensor & Sensing Networ Coll Elect Informat & Opt Engn Tianjin 300350 Peoples R China Guilin Univ Elect Technol Sch Artificial Intelligence Guilin Guangxi Peoples R China Tianjin Huanhu Hosp Key Lab Cerebral Artery & Neural Degenerat Tianji Dept Med Imaging Tianjin Peoples R China

Acute ischemic stroke is a common threat to human health and may obtain timely treatment by fast localizing and quantitatively evaluating the lesions. Most CNN-based methods try to segment and measure the lesions, however, they require a training on a large number of labeled subjects that are labor-intensive and time-consuming to obtain. In this paper, a method is proposed that can combine limited labeled subjects with abundant unlabeled subjects to alleviate the problem. The proposed method consists of two stages: stepwise learning process and segmentation process. Stepwise learning is used to obtain the pretrained encoder. The pretrained encoder and the proposed decoder are connected into a new end-to-end segmentation network, which is retrained on the labeled subjects in the segmentation process. By using 5 labeled subjects and 79 unlabeled subjects, the proposed method achieves a mean dice coefficient of 0.663 +/-$\,{\pm}\,$0.205, a mean average symmetric surface distance (ASSD) of 2.17 mm and a mean 95 percentile Hausdorff distance (HD) of 18.38 mm on a clinical MR dataset with 179 subjects. More importantly, it achieves lesion-wise F-1 score of 0.857 and a subject-wise detection rate of 0.966.

关键词： end-to-end segmentation network medical image processing convolutional neural nets image segmentation labeled MR image unlabeled MR image CNN acute ischemic stroke lesion biomedical MRI stepwise learning process learning (artificial intelligence) encoder-decoder diseases segmentation process

来源：评论

学校读者我要写书评

暂无评论

D-SELD: Dataset-Scalable Exemplar LCA-decoder

引用

NEUROMORPHIC COMPUTING AND ENGINEERING 2024年第4期4卷 044009-044009页

作者： Takaghaj, Sanaz Mahmoodi Sampson, Jack Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA Penn State Univ Sch Engn Design & Innovat University Pk PA 16802 USA

Neuromorphic computing has recently gained significant attention as a promising approach for developing energy-efficient, massively parallel computing systems inspired by the spiking behavior of the human brain and natively mapping spiking neural networks (SNNs). Effective training algorithms for SNNs are imperative for increased adoption of neuromorphic platforms;however, SNN training continues to lag behind advances in other classes of ANN. In this paper, we reduce this gap by proposing an innovative encoder-decoder technique that leverages sparse coding and the locally competitive algorithm (LCA) to provide an algorithm specifically designed for neuromorphic platforms. Using our proposed Dataset-Scalable Exemplar LCA-decoder we reduce the computational demands and memory requirements associated with training SNNs using error backpropagation methods on increasingly larger training sets. We offer a solution that can be scalably applied to datasets of any size. Our results show the highest reported top-1 test accuracy using SNNs on the ImageNet and CIFAR100 datasets, surpassing previous benchmarks. Specifically, we achieved a record top-1 accuracy of 80.75% on ImageNet (ILSVRC2012 validation set) and 79.32% on CIFAR100 using SNNs.

关键词： encoder-decoder LCA exemplar learning spiking neural networks sparse coding neuromorphic computing

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：