Multi-User Semantic Fusion for Semantic Communications over Degraded Broadcast Channels

Degraded broadcast channels(DBC) are a typical multiuser communication scenario, Semantic communications over DBC still lack in-depth research. In this paper, we design a semantic communications approach based on multi-user semantic fusion for wireless image transmission over DBC. The transmitter extracts semantic features for two users separately and then effectively fuses them for broadcasting by leveraging semantic similarity. Unlike traditional allocation of time, power, or bandwidth, the semantic fusion scheme can dynamically control the weight of the semantic features of the two users to balance their performance. Considering the different channel state information(CSI) of both users over DBC,a DBC-Aware method is developed that embeds the CSI of both users into the joint source-channel coding encoder and fusion module to adapt to the channel.Experimental results show that the proposed system outperforms the traditional broadcasting schemes.

FusionNN:A Semantic Feature Fusion Model Based on Multimodal for Web Anomaly Detection

With the rapid development of the mobile communication and the Internet,the previous web anomaly detectionand identificationmodels were built relying on security experts’empirical knowledge and attack features.Althoughthis approach can achieve higher detection performance,it requires huge human labor and resources to maintainthe feature library.In contrast,semantic feature engineering can dynamically discover new semantic featuresand optimize feature selection by automatically analyzing the semantic information contained in the data itself,thus reducing dependence on prior knowledge.However,current semantic features still have the problem ofsemantic expression singularity,as they are extracted from a single semantic mode such as word segmentation,character segmentation,or arbitrary semantic feature extraction.This paper extracts features of web requestsfrom dual semantic granularity,and proposes a semantic feature fusion method to solve the above problems.Themethod first preprocesses web requests,and extracts word-level and character-level semantic features of URLs viaconvolutional neural network(CNN),respectively.By constructing three loss functions to reduce losses betweenfeatures,labels and categories.Experiments on the HTTP CSIC 2010,Malicious URLs and HttpParams datasetsverify the proposedmethod.Results show that compared withmachine learning,deep learningmethods and BERTmodel,the proposed method has better detection performance.And it achieved the best detection rate of 99.16%in the dataset HttpParams.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

Semantic Segmentation Based Remote Sensing Data Fusion on Crops Detection

Data fusion is usually an important process in multi-sensor remotely sensed imagery integration environments with the aim of enriching features lacking in the sensors involved in the fusion process. This technique has attracted much interest in many researches especially in the field of agriculture. On the other hand, deep learning (DL) based semantic segmentation shows high performance in remote sensing classification, and it requires large datasets in a supervised learning way. In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops. Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process. The experiment results show that the crops extraction with an average overall accuracy more than 93% has been obtained, which demonstrates that data fusion combined with DL is highly feasible to crops extraction from satellite images and GE imagery, and it shows that deep learning techniques can serve as an invaluable tools for larger remote sensing data fusion frameworks, specifically for the applications in precision farming.

PowerDetector:Malicious PowerShell Script Family Classification Based on Multi-Modal Semantic Fusion and Deep Learning

Power Shell has been widely deployed in fileless malware and advanced persistent threat(APT)attacks due to its high stealthiness and live-off-theland technique.However,existing works mainly focus on deobfuscation and malicious detection,lacking the malicious Power Shell families classification and behavior analysis.Moreover,the state-of-the-art methods fail to capture fine-grained features and semantic relationships,resulting in low robustness and accuracy.To this end,we propose Power Detector,a novel malicious Power Shell script detector based on multimodal semantic fusion and deep learning.Specifically,we design four feature extraction methods to extract key features from character,token,abstract syntax tree(AST),and semantic knowledge graph.Then,we intelligently design four embeddings(i.e.,Char2Vec,Token2Vec,AST2Vec,and Rela2Vec) and construct a multi-modal fusion algorithm to concatenate feature vectors from different views.Finally,we propose a combined model based on transformer and CNN-Bi LSTM to implement Power Shell family detection.Our experiments with five types of Power Shell attacks show that PowerDetector can accurately detect various obfuscated and stealth PowerShell scripts,with a 0.9402 precision,a 0.9358 recall,and a 0.9374 F1-score.Furthermore,through singlemodal and multi-modal comparison experiments,we demonstrate that PowerDetector’s multi-modal embedding and deep learning model can achieve better accuracy and even identify more unknown attacks.

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%.

ST-SIGMA:Spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting

Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.

DuFNet:Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology.Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis.Semantic segmentation is also a challenging technology for image understanding and scene parsing.We focused on the challenging task of real-time semantic segmentation in this paper.In this paper,we proposed a novel fast architecture for real-time semantic segmentation named DuFNet.Starting from the existing work of Bilateral Segmentation Network(BiSeNet),DuFNet proposes a novel Semantic Information Flow(SIF)structure for context information and a novel Fringe Information Flow(FIF)structure for spatial information.We also proposed two kinds of SIF with cascaded and paralleled structures,respectively.The SIF encodes the input stage by stage in the ResNet18 backbone and provides context information for the feature fusionmodule.Features from previous stages usually contain rich low-level details but high-level semantics for later stages.Themultiple convolutions embed in Parallel SIF aggregate the corresponding features among different stages and generate a powerful global context representation with less computational cost.The FIF consists of a pooling layer and an upsampling operator followed by projection convolution layer.The concise component provides more spatial details for the network.Compared with BiSeNet,our work achieved faster speed and comparable performance with 72.34%mIoU accuracy and 78 FPS on Cityscapes Dataset based on the ResNet18 backbone.

A NOVEL FRAMEWORK FOR SOCCER GOAL DETECTION BASED ON SEMANTIC RULE

Focusing on the problem of goal event detection in soccer videos,a novel method based on Hidden Markov Model(HMM) and the semantic rule is proposed.Firstly,a HMM for a goal event is constructed.Then a Normalized Semantic Weighted Sum(NSWS) rule is established by defining a new feature of shots,semantic observation weight.The test video is detected based on the HMM and the NSWS rule,respectively.Finally,a fusion scheme based on logic distance is proposed and the detection results of the HMM and the NSWS rule are fused by optimal weights in the decision level,obtaining the final result.Experimental results indicate that the proposed method achieves 96.43% precision and 100% recall,which shows the effectiveness of this letter.

Semantic-Based Video Retrieval Survey

There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. Thus, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, and video-on-demand broadcasting. In this paper, the different approaches of video retrieval are outlined and briefly categorized. Moreover, the different methods that bridge the semantic gap in video retrieval are discussed in more details.

Optimized Deep Learning Model for Fire Semantic Segmentation

Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.

基于视觉与文本语义增强的多模态命名实体识别方法

为了解决视觉特征和文本特征融合后存在部分语义缺失从而导致视觉信息对文本信息的补充有较大偏差的问题,提出了一种基于视觉与文本语义增强的多模态命名实体识别方法。融合BERT文本特征提取和CLIP(contrastive language–image pre-training)视觉特征提取方法,设计了基于协同交叉注意力机制的特征交互单元,以增强视觉信息和文本信息之间的语义关系。CLIP通过对比学习框架进行预训练,优化模型以正确匹配视觉和对应的文本描述,最大化正样本(匹配的视觉-文本对)的相似性,同时最小化负样本(不匹配的视觉-文本对)的相似性。采用通用领域数据集TWITTER-2015和TWITTER-2017作为实验数据集。实验结果表明,本模型相比传统方法在多模态命名实体识别任务中的准确率、召回率、F1值均有显著提升。

双通道特征融合的真实场景点云语义分割方法

真实场景点云不仅具有点云的空间几何信息,还具有三维物体的颜色信息,现有的网络无法有效利用真实场景的局部特征以及空间几何特征信息,因此提出了一种双通道特征融合的真实场景点云语义分割方法DCFNet(dual-channel feature fusion of real scene for point cloud semantic segmentation)可用于不同场景下的室内外场景语义分割。更具体地说,为了解决不能充分提取真实场景点云颜色信息的问题,该方法采用上下两个输入通道,通道均采用相同的特征提取网络结构,其中上通道的输入是完整RGB颜色和点云坐标信息,该通道主要关注于复杂物体对象场景特征,下通道仅输入点云坐标信息,该通道主要关注于点云的空间几何特征;在每个通道中为了更好地提取局部与全局信息,改善网络性能,引入了层间融合模块和Transformer通道特征扩充模块;同时,针对现有的三维点云语义分割方法缺乏关注局部特征与全局特征的联系,导致对复杂场景的分割效果不佳的问题,对上下两个通道所提取的特征通过DCFFS(dual-channel feature fusion segmentation)模块进行融合,并对真实场景进行语义分割。对室内复杂场景和大规模室内外场景点云分割基准进行了实验,实验结果表明,提出的DCFNet分割方法在S3DIS Area5室内场景数据集以及STPLS3D室外场景数据集上,平均交并比(MIOU)分别达到71.18%和48.87%,平均准确率(MACC)和整体准确率(OACC)分别达到77.01%与86.91%,实现了真实场景的高精度点云语义分割。

基于改进DeepLabv3+的轻量化作物杂草识别方法

为在存储资源与计算能力有限的设备上实现田间作物和杂草的识别,本文提出一种基于改进DeepLabv3+的轻量化语义分割网络。首先,以MobileNet v2作为DeepLabv3+的特征提取骨干网络,提出双分支残差模块替换倒残差模块,并删除后两层卷积以降低模型参数量。其次,在空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块中引入分组逐点卷积,使用深度扩张卷积替换标准卷积,并将卷积后的特征图进行多尺度特征融合增强对作物和杂草深层特征的提取能力。最后,将原有的非线性激活函数替换为Leaky ReLU激活函数来提升分割精度。实验结果表明:改进后网络的mIOU达到86.75%,参数量仅为0.69M,FPS达到了98,与原始DeepLabv3+以及3个典型轻量化语义分割网络的相比,参数量最小,在对比的轻量化网络中具有最高的分割精度。

基于脊柱融合器设计的椎骨分割及测量方法

由于脊柱融合器设计的类型与尺寸受到椎骨区域及参数的影响,为制造个性化脊柱融合器,提出构建含有语义参数信息的分割模板,对脊椎骨进行由粗到细的分割和语义参数测量。首先基于生成的几何平均化骨骼,引入形态学,对其进行分割并获取骨架线比例,构建分割模板;其次,标注语义特征点,计算语义参数,生成含有语义参数信息的分割模板;然后,基于分割模板,利用骨架线与测地环,结合脊椎整体形态与外表面,实现对目标骨骼的区域分割,为脊柱融合器类型的选择提供指导;最后将模板与目标骨骼进行配准、映射,实现目标骨骼语义参数的自动测量,辅助确定脊柱融合器尺寸。以不同脊椎骨进行实验,验证了方法的合理性与有效性,为脊柱融合器的设计与制造奠定了理论基础。

面向视频数据的多模态情感分析

多模态情感分析旨在从文本、图像和音频数据中提取和整合语义信息,从而识别在线视频中说话者的情感状态。尽管多模态融合方案在此研究领域已取得一定成果,但是已有方法在处理模态间分布差异和关系知识的融合方面仍有欠缺,为此,提出一种多模态情感分析方法。设计一种多模态提示门(MPG)模块,其能够将非语言信息转换为融合文本上下文的提示,利用文本信息对非语言信号的噪声进行过滤,得到包含丰富语义信息的提示,以增强模态间的信息整合。此外,提出一种实例到标签的对比学习框架,在语义层面上区分隐空间中的不同标签以进一步优化模型输出。在3个大规模情感分析数据集上的实验结果表明,该方法的二分类精度相对次优模型提高了约0.7%,三分类精度提高了超过2.5%,达到0.671。该方法能够为将多模态情感分析引入用户画像、视频理解、AI面试等领域提供参考。

基于细节增强的双分支实时语义分割网络

实时语义分割方法常利用双分支结构分别保存图像的浅层空间信息和深层语义信息。然而,当前基于双分支结构的实时语义分割方法重点研究语义特征的挖掘,忽略了空间特征的保持,导致网络无法精准地捕捉图像内物体的边界和纹理等细节特征,最终分割效果欠佳。针对以上问题,提出基于细节增强的双分支实时语义分割网络(DEDBNet),多阶段增强空间细节信息。首先,提出细节增强双向交互(DEBIM)模块,在分支间的交互阶段使用轻量空间注意力机制增强高分辨率特征图对细节信息的表达能力,促进空间细节特征在高低两分支上的流动,以加强网络对细节信息的学习能力;其次,设计局部细节注意力特征融合模块(LDAFF),在两分支末端特征融合的过程中同时建模全局语义信息和局部空间信息,解决不同层次特征图之间细节不连续的问题;此外,引入边界损失,在不影响模型速度的情况下引导网络浅层学习物体边界信息。所提网络在Cityscapes验证集上以92.3 frame/s的帧速率(FPS)获得78.2%的平均交并比(mIoU),在CamVid测试集上以202.8 frame/s获得79.2%的mIoU;与深度双分辨率网络(DDRNet-23-slim)相比,mIoU分别提高了1.1和4.5个百分点。实验结果表明,DEDBNet能够准确地分割场景图像,且满足实时性要求。

基于注意力机制及多分支特征融合的实时语义分割算法

为了合理平衡语义分割中的精确度与实时性,基于快速卷积神经网络模型(Fast-SCNN)提出了一种基于注意力机制及多分支特征融合的实时语义分割算法模型。该算法模型首先通过注意力模块捕获空间特征之间的相互联系,增强空间细节信息;然后合理设计融合模块,最大化利用各分支信息,实现深层特征与浅层特征更好的融合;最后引入自适应特征增强注意力模块,捕获长距离像素间的相互依赖关系。实验结果表明,文中算法模型在Cityscapes数据集上获得了71.55%的分割精度,推理速度FPS达到97.6帧/s,模型参数量为1.39 M,验证了该算法所构成网络模型的有效性。

基于异构图和语义融合的实体关系抽取

关系抽取是信息抽取中的一项重要任务,其目的是从非结构化文本中抽取出所有关系三元组。然而,如何有效地处理这一问题仍然是一个挑战,特别是对于关系重叠问题。为了有效处理重叠问题,该文提出一种基于异构图和语义融合的实体关系抽取方法:使用异构图将关系信息作为先验知识融入词表示,增强词表示的表示能力,使得模型能有效地处理单词实体重叠问题;使用语义融合模块将不同层次特征融合在一起作为关系分类模型的输入,使得模型能够有效地处理实体对重叠问题。所提方法在NYT和WebNLG数据集上取得了最好的效果,详细的实验也表明所提方法可以处理复杂的场景。