Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation

The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate diseases.In recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant challenges.In this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI images.We propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to decoder.In the decoder stage,a feature fusion module is proposed to obtain global context information.We evaluate our model on MRI images of the prostate acquired from a local hospital.The relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,respectively.To quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique AGMSF-Net.The essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.

Attention Guided Food Recognition via Multi-Stage Local Feature Fusion

The task of food image recognition,a nuanced subset of fine-grained image recognition,grapples with substantial intra-class variation and minimal inter-class differences.These challenges are compounded by the irregular and multi-scale nature of food images.Addressing these complexities,our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion,grounded in the ConvNeXt architecture.Our model employs hybrid attention(HA)mechanisms to pinpoint critical discriminative regions within images,substantially mitigating the influence of background noise.Furthermore,it introduces a multi-stage local fusion(MSLF)module,fostering long-distance dependencies between feature maps at varying stages.This approach facilitates the assimilation of complementary features across scales,significantly bolstering the model’s capacity for feature extraction.Furthermore,we constructed a dataset named Roushi60,which consists of 60 different categories of common meat dishes.Empirical evaluation of the ETH Food-101,ChineseFoodNet,and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%,82.86%,and 92.50%,respectively.These figures not only mark an improvement of 1.04%,3.42%,and 1.36%over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods.Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition,setting a new benchmark for the field.

Fusion of Convolutional Self-Attention and Cross-Dimensional Feature Transformationfor Human Posture Estimation

Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which is crucial for intelligent applications,contradicts the lowdetection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significantfeature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,wherethe attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.Theresults show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and3.9%in AP^(M).This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.

基于改进蜣螂算法优化CNN-BiLSTM-Attention的串联电弧故障检测方法

针对故障电弧特征提取不足、检测精度不高等问题,提出一种多特征融合的改进蜣螂算法(IDBO)优化融合注意力(Attention)机制的卷积神经网络(CNN)和双向长短期记忆(BiLSTM)神经网络的串联电弧故障检测方法。通过实验平台提取电流的时域、频域、时频域以及信号自回归参数模型特征;利用核主成分分析(KPCA)对特征进行降维融合,并将求取的特征向量作为CNN-BiLSTM-Attention的输入向量;引入Cubic混沌映射、螺旋搜索策略、动态权重系数、高斯柯西变异策略对蜣螂算法进行改进,利用改进蜣螂算法对CNN-BiLSTM-Attention超参数优化实现串联电弧故障诊断。结果表明,所提方法故障电弧检测准确率达到97.92%,可高效识别串联电弧故障。

基于Attention-BiTCN的网络入侵检测方法

为解决网络入侵检测领域多分类准确率不高的问题,文章根据网络流量数据具有时序特征的特点,提出一种基于注意力机制和双向时间卷积神经网络(BiDirectional Temporal Convolutional Network,BiTCN)的网络入侵检测模型。首先,该模型对数据集进行独热编码和归一化处置等预处理,解决网络流量数据离散性强和标度不统一的问题;其次,将预处理好的数据经双向滑窗法生成双向序列,并同步输入Attention-Bi TCN模型中;然后,提取双向时序特征并通过加性方式融合,得到时序信息被增强后的融合特征;最后,使用Softmax函数对融合特征进行多种攻击行为检测识别。文章所提模型在NSL-KDD和UNSW-NB15数据集上进行实验验证,多分类准确率分别达到99.70%和84.07%,优于传统网络入侵检测算法,且比其他深度学习模型在检测性能上有显著提升。

Image Inpainting Technique Incorporating Edge Prior and Attention Mechanism

Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.

Deep Global Multiple-Scale and Local Patches Attention Dual-Branch Network for Pose-Invariant Facial Expression Recognition

Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

Bridge Crack Segmentation Method Based on Parallel Attention Mechanism and Multi-Scale Features Fusion

Regular inspection of bridge cracks is crucial to bridge maintenance and repair.The traditional manual crack detection methods are timeconsuming,dangerous and subjective.At the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and speed.Therefore,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network framework.First,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the model.Moreover,we introduce the parallel attention mechanism into the encoding and decoding stages.The attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various noises.Finally,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion module.Our research results are validated on the self-made dataset.The experiments show that our method is more accurate than other methods.Its intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,respectively.In addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/s.The results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods.

Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

The diagnosis of COVID-19 requires chest computed tomography(CT).High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease,so it is of clinical importance to study super-resolution(SR)algorithms applied to CT images to improve the reso-lution of CT images.However,most of the existing SR algorithms are studied based on natural images,which are not suitable for medical images;and most of these algorithms improve the reconstruction quality by increasing the network depth,which is not suitable for machines with limited resources.To alleviate these issues,we propose a residual feature attentional fusion network for lightweight chest CT image super-resolution(RFAFN).Specifically,we design a contextual feature extraction block(CFEB)that can extract CT image features more efficiently and accurately than ordinary residual blocks.In addition,we propose a feature-weighted cascading strategy(FWCS)based on attentional feature fusion blocks(AFFB)to utilize the high-frequency detail information extracted by CFEB as much as possible via selectively fusing adjacent level feature information.Finally,we suggest a global hierarchical feature fusion strategy(GHFFS),which can utilize the hierarchical features more effectively than dense concatenation by progressively aggregating the feature information at various levels.Numerous experiments show that our method performs better than most of the state-of-the-art(SOTA)methods on the COVID-19 chest CT dataset.In detail,the peak signal-to-noise ratio(PSNR)is 0.11 dB and 0.47 dB higher on CTtest1 and CTtest2 at×3 SR compared to the suboptimal method,but the number of parameters and multi-adds are reduced by 22K and 0.43G,respectively.Our method can better recover chest CT image quality with fewer computational resources and effectively assist in COVID-19.

基于SBERT-Attention-LDA与ML-LSTM特征融合的烟草问句意图识别方法

针对烟草领域中问句意图识别存在的特征稀疏、术语繁多和捕捉文本内部的语义关联困难等问题,提出了一种基于SBERT-Attention-LDA(Sentence-bidirectional encoder representational from transformers-Attention mechanism-Latent dirichlet allocation)与ML-LSTM(Multi layers-Long short term memory)特征融合的问句意图识别方法。该方法首先基于SBERT预训练模型和Attention机制对烟草问句进行动态编码,转换为富含语义信息的特征向量,同时利用LDA模型建模出问句的主题向量,捕捉问句中的主题信息;然后通过更改后的模型级特征融合方法ML-LSTM获得具有更为完整、准确问句语义的联合特征表示;再使用3通道的卷积神经网络(Convolutional neural network,CNN)提取问句混合语义表示中隐藏特征,输入到全连接层和Softmax函数中实现对问句意图的分类。基于烟草行业权威网站上获取的数据集开展了实验验证,实验结果表明,所提方法相比其他几种深度学习结合注意力机制的方法精确率、召回率和F1值上有显著提升,与BERT和ERNIE(Enhanced representation through knowledge integration and embedding)-CNN模型相比提升明显,F1值分别提升2.07、2.88个百分点。

融合PMV物理方程和Attention-LSTM神经网络的铁路客站旅客舒适度模型研究

铁路客站的舒适度直接关系着旅客的出行体验和满意度。选取聊城西站作为研究对象,采用PMV物理方程、Attention-LSTM神经网络模型以及PMV&Attention-LSTM融合模型3种方法,针对旅客舒适度开展综合评估与分析。在模型构建过程中,运用了标准化处理、数据集划分、网格搜索交叉验证等技术寻找最佳超参数,并记录了训练过程中的损失函数和均方误差。在模型预测中,充分考虑了温度、湿度、风速、空气质量、二氧化碳、光照、噪声等环境因素对旅客舒适度的影响。对比3种预测方法,结果显示,融合模型在考虑多维环境数据时可更准确地反映舒适度水平,表明该模型更适应铁路客站的复杂环境条件,可为提高候车厅舒适性提供更为可靠的参考依据。

联合Self-attention与Axial-attention的机场跑道裂缝分割

机场跑道裂缝形态多样、方向各异、长短不一且粗细不均,通常不具有统计规律。现有的各类裂缝分割算法难以在此类复杂场景中落地。针对上述问题,提出了联合self-attention与axial-attention的机场跑道裂缝分割网络(CSA-net),通过引入自注意力模块、轴向注意力模块、可变形卷积模块,提取裂缝的局部特征和全局语义特征。通过transformer decoder还原特征图的原始尺寸,融合了不同尺度间的分割结果,保留尽可能多的细节信息,使得CSA-net有更好的分割精度。在机场跑道实拍的数据集上进行的测试表明,针对裂缝的像素级分割指标F1-score达到了78.91%,高于目前各类裂缝分割算法。

基于改进Centerfusion的自动驾驶3D目标检测模型

针对自动驾驶路面上目标漏检和错检的问题,提出一种基于改进Centerfusion的自动驾驶3D目标检测模型。该模型通过将相机信息和雷达特征融合,构成多通道特征数据输入,从而增强目标检测网络的鲁棒性,减少漏检问题;为了能够得到更加准确丰富的3D目标检测信息,引入了改进的注意力机制,用于增强视锥网格中的雷达点云和视觉信息融合;使用改进的损失函数优化边框预测的准确度。在Nuscenes数据集上进行模型验证和对比,实验结果表明,相较于传统的Centerfusion模型,提出的模型平均检测精度均值(mean Average Precision,mAP)提高了1.3%,Nuscenes检测分数(Nuscenes Detection Scores,NDS)提高了1.2%。

Bilateral U-Net semantic segmentation with spatial attention mechanism

Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small-scale samples,a bilateral U-Net network model with a spatial attention mechanism is designed.The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial Attention(APSA)module compared to the Attenuated Spatial Pyramid module,which can increase the receptive field and enhance the information,and finally adds the context fusion prediction branch that fuses high-semantic and low-semantic prediction results,and the model effectively improves the segmentation accuracy of small data sets.The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks,the algorithm has a better segmentation effect and segmentation accuracy,and its mIOU reaches 75.85%.Moreover,to verify the generality of the model and the effectiveness of the APSA module,experiments were conducted on the VOC 2012 data set,and the APSA module improved mIOU by about 12.2%.

AF-Net:A Medical Image Segmentation Network Based on Attention Mechanism and Feature Fusion

Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods.

Transformer architecture based on mutual attention for image-anomaly detection

Image-anomaly detection, which is widely used in industrial fields. Previous studies that attempted to address this problem often trained convolutional neural network-based models(e.g., autoencoders and generative adversarial networks) to reconstruct covered parts of input images and calculate the difference between the input and reconstructed images. However, convolutional operations are effective at extracting local features, making it difficult to identify larger image anomalies. Method To this end, we propose a transformer architecture based on mutual attention for image-anomaly separation. This architecture can capture long-term dependencies and fuse local and global features to facilitate better image-anomaly detection. Result Our method was extensively evaluated on several benchmarks, and experimental results showed that it improved the detection capability by 3.1% and localization capability by 1.0% compared with state-of-the-art reconstruction-based methods.

Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

Infrasound Event Classification Fusion Model Based on Multiscale SE-CNN and BiLSTM

The classification of infrasound events has considerable importance in improving the capability to identify the types of natural disasters.The traditional infrasound classification mainly relies on machine learning algorithms after artificial feature extraction.However,guaranteeing the effectiveness of the extracted features is difficult.The current trend focuses on using a convolution neural network to automatically extract features for classification.This method can be used to extract signal spatial features automatically through a convolution kernel;however,infrasound signals contain not only spatial information but also temporal information when used as a time series.These extracted temporal features are also crucial.If only a convolution neural network is used,then the time dependence of the infrasound sequence will be missed.Using long short-term memory networks can compensate for the missing time-series features but induces spatial feature information loss of the infrasound signal.A multiscale squeeze excitation–convolution neural network–bidirectional long short-term memory network infrasound event classification fusion model is proposed in this study to address these problems.This model automatically extracted temporal and spatial features,adaptively selected features,and also realized the fusion of the two types of features.Experimental results showed that the classification accuracy of the model was more than 98%,thus verifying the effectiveness and superiority of the proposed model.