搜索到2425篇“ CONTEXT-AWARE“的相关文章
Context-Aware Augmented Reality Using Human-Computer Interaction Models
2024年
Augmented Reality is a technique that allows users to overlap digital information with their physical world.The Augmented Reality(AR)displays have an exceptional characteristic from the Human–Computer Interaction(HCI)perspective.Due to its increasing popularity and application in diverse domains,increasing user-friendliness and AR usage are critical.Context-aware is one approach since an AR application can adapt to the user,environment,needs and enhance ergonomic principles and functionality.This paper proposes the Intelligent Contextaware Augmented Reality Model(ICAARM)for Human–Computer Interaction systems.This study explores and reduces interaction uncertainty by semantically modeling user-specific interaction with context,allowing personalised interaction.Sensory information is captured from an AR device to understand user interactions and context.These depictions carry semantics to Augmented Reality applications about the user’s intention to interact with a specific device affordance.Thus,this study describes personalised gesture interaction in VR/AR applications for immersive/intelligent environments.
Ying SunQiongqiong GuoShumei ZhaoKarthik ChandranG.Fathima
关键词:CONTEXT-AWARE
Context-Aware Feature Extraction Network for High-Precision UAV-Based Vehicle Detection in Urban Environments
2024年
The integration of Unmanned Aerial Vehicles(UAVs)into Intelligent Transportation Systems(ITS)holds trans-formative potential for real-time traffic monitoring,a critical component of emerging smart city infrastructure.UAVs offer unique advantages over stationary traffic cameras,including greater flexibility in monitoring large and dynamic urban areas.However,detecting small,densely packed vehicles in UAV imagery remains a significant challenge due to occlusion,variations in lighting,and the complexity of urban landscapes.Conventional models often struggle with these issues,leading to inaccurate detections and reduced performance in practical applications.To address these challenges,this paper introduces CFEMNet,an advanced deep learning model specifically designed for high-precision vehicle detection in complex urban environments.CFEMNet is built on the High-Resolution Network(HRNet)architecture and integrates a Context-aware Feature Extraction Module(CFEM),which combines multi-scale feature learning with a novel Self-Attention and Convolution layer setup within a Multi-scale Feature Block(MFB).This combination allows CFEMNet to accurately capture fine-grained details across varying scales,crucial for detecting small or partially occluded vehicles.Furthermore,the model incorporates an Equivalent Feed-Forward Network(EFFN)Block to ensure robust extraction of both spatial and semantic features,enhancing its ability to distinguish vehicles from similar objects.To optimize computational efficiency,CFEMNet employs a local window adaptation of Multi-head Self-Attention(MSA),which reduces memory overhead without sacrificing detection accuracy.Extensive experimental evaluations on the UAVDT and VisDrone-DET2018 datasets confirm CFEMNet’s superior performance in vehicle detection compared to existing models.This new architecture establishes CFEMNet as a benchmark for UAV-enabled traffic management,offering enhanced precision,reduced computational demands,and scalability for deployment in smart city applications.The advan
Yahia SaidYahya AlassafTaoufik SaidaniRefka GhodhbaniOlfa Ben RhaiemAli Ahmad Alalawi
关键词:UAVS
基于上下文感知的强化学习AUV控制器研究
2024年
为了提升基于强化学习的自主水下航行器(Autonomous Underwater vehicle,AUV)控制器在复杂海况中对环境干扰的鲁棒性,设计一种利用上下文信息进行环境感知的强化学习控制器。结合水下机器人运动学及动力学方程对深度跟踪任务进行建模,构建了基于PPO-clip算法的深度控制器,并在算法中融入了上下文变量和域随机化方法。在仿真环境中分别进行海流干扰、暗涌干扰以及两者共同干扰环境的深度跟踪任务,仿真结果表明,本文提出的方法对强化学习控制器的抗干扰能力有明显的提升,在多种环境干扰下更精准地完成深度跟踪任务。
徐春晖徐春晖周仕昊杨士霖
关键词:AUV上下文感知
知识情境感知的深度知识追踪模型
2024年
知识追踪通过学习者历史作答数据动态追踪学习者的认知状态并预测他们未来的答题表现,然而,现有的知识追踪模型通常只利用试题中考查的知识点来表征,没有考虑试题本身蕴含的重要知识情境特征,这限制了模型的效果.此外,和融合教育先验的认知诊断方法相比,知识追踪模型的可解释性略有不足.为了解决上述问题,提出一种知识情境感知的深度知识追踪模型,通过知识情境表征模块来获取试题深层次的知识权重、试题难度等知识情境特征.在知识聚合模块中,模型将知识权重嵌入学习者面向试题的作答能力的计算,最后,在学习预测模型中引入猜测和失误因素,通过认知诊断模型来优化实际场景中的预测表现,进一步提高模型的预测性能.和现有方法相比,提出的模型在试题层级上取得了更好的预测结果,同时体现了模型可解释性方面的优势.
蒲杰张所娟陈卫卫
关键词:试题难度
融合情景感知的智慧图书馆阅读推荐服务研究
2024年
探索融合情景感知的智慧图书馆阅读推荐服务,不仅能够解决图书馆用户流失问题、提升图书馆阅读推广服务质量和成效,还可以进一步优化图书馆阅读资源推荐模式、促进资源的综合利用和共享传播。文章在阐述智慧图书馆内涵、特征和情景感知服务研究现状的基础上,分析融合情景感知的智慧图书馆阅读推荐服务优势,构建了融合情景感知的智慧图书馆阅读推荐服务体系,从增强用户信息安全、提高资源质量和效用、完善服务反馈与评价以及加强馆员胜任能力建设4个方面提出了服务的保障措施。
马英李琦王岚宁杨春壮
关键词:情景感知个性化服务
Multi-scale context-aware network for continuous sign language recognition
2024年
The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.
Senhua XUELiqing GAOLiang WANWei FENG
CALTM:A Context-Aware Long-Term Time-Series Forecasting Model
2024年
Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series forecasting.However,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term forecastings.Consequently,the effectiveness of existing methods diminishes in such scenarios.Therefore,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow.Our model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final forecasting.Experimental results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
Canghong JinJiapeng ChenShuyu WuHao WuShuoping WangJing Ying
一种用于视觉跟踪的低秩上下文感知的相关滤波器
2024年
基于DCF的目标跟踪方法在保持实时运行时,由于在精度和鲁棒性之间实现了很好的权衡而备受关注。但是,当出现遮挡、移出视野、平面外旋转等干扰时,现有跟踪器仍面临着模型漂移甚至跟踪失败的情况。为此,提出了一种基于低秩上下文感知的相关滤波器LR_CACF。具体来说,在滤波器学习阶段,直接将目标及其上下文信息集成到DCF框架中,以更好地将目标从背景中鉴别出来;同时,对跨帧视频施加低秩约束以强调时序平滑性,使得学习的滤波器处于一个低维的鉴别流行上,进一步提高了跟踪性能;然后,利用ADMM实现滤波模型的高效优化;此外,针对模型失真的问题,启动多模态检测机制来识别响应图的可靠性,当反馈不可靠时,滤波器停止训练,同时扩大搜索区域,并采用区域重叠的方法重新捕获目标。在OTB-50,OTB-100和DTB70数据集上进行了大量实验,实验结果表明,相对于基线SAMF_CA,在DP方面,LR_CACF分别获得了6.9%,4.0%和7.1%的增益,AUC分别提高了3.6%,2.7%和5.4%。基于属性分析的结果表明,LR_CACF尤其擅长处理遮挡、移出视野、平面外旋转、低分辨率和快速运动等场景。
苏银强王宣王淳李充徐芳
关键词:视觉跟踪相关滤波上下文感知
一种基于Context-Aware的非机动车密度估计方法
本发明提供一种基于Context‑Aware的非机动车密度估计方法,首先,收集与非机动车相关的有效素材,构建初始数据集,采用Mixup结合Albumentations的数据增强方法,获得增强数据集。然后,对图片进行裁剪,...
柯逍郑龙辉
基于交叉注意力多源数据增强的情境感知查询建议方法
2024年
当前基于神经网络模型的查询建议研究往往单独采用查询日志会话中的查询序列作为训练数据,但由于查询本身缺乏句法关系,甚至缺失语义,导致神经网络模型不能充分挖掘和推理查询序列中各种词或概念之间语义关系。针对这一问题,提出一种基于交叉注意力多源数据增强(MDACA)的Transformer模型框架,用于生成情境感知的查询建议。采用基于Transformer的编码器-解码器模型,利用交叉注意力机制,融合了查询层、文档语义层以及全局查询建议信息。实验结果表明,与目前方法相比,该方法能生成具有更高相关性的情境感知查询建议。
张乃洲曹薇
关键词:情境感知

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