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441.
随着商家评论网站的快速发展,网站上的内容越来越多,用户难以在短时间内获取到有价值的评论.引入了一项名为“多模态定制化评论生成”的新任务.该任务旨在为特定用户生成他们尚未评价的产品的定制化评论,这有助于用户对特定产品提供宝贵的意见.为了实现这一目标,探索了一种基于预训练语言模型的多… …   相似文献
442.
针对多模态情感分析中模态异构、贡献动态与语义抽象不足等问题,提出一种三阶段闭环融合模型MIFA,路径包含“统一对齐—动态融合调控—高阶语义抽象”。方法上,首先以统一语义对齐实现异构模态在共享空间的一致表达;继而通过上下文门控与通道调制联合估计模态/通道权重;最终以分层残差语义增强… …   相似文献
443.
针对语音情感识别中语音信息利用不充分的问题,提出了一种融合语音多层次特征的语音情绪识别模型MFTA（multi-level feature based temporal attention network）,该模型能够处理语音的多种特征,最终进行特征融合,引入的时间注意力机制利用… …   相似文献
444.
针对传统RRT*算法在进行路径规划时存在效率低下以及路径质量不高等问题,提出一种改进算法CDBA-RRT*（cooperative dynamic Bi-APF-RRT*）。该方法维护两棵双向搜索树并采用多策略协同扩展。首先构建长轴固定、短轴随迭代次数动态增大的椭圆采样区域以及采… …   相似文献
445.
针对复杂室内场景中经典三维重建方法存在细节表征不足、边缘模糊、混叠伪影等问题,提出一种基于三维高斯溅射（3DGS）的复杂室内场景三维重建算法。首先提出前置点云增密网络对运动结构恢复的稀疏点云进行增密补全,构建高分辨率的增强3D高斯辐射场,提高对复杂场景细节部分的表征精度;随后引入… …   相似文献
446.
针对现有分布式拒绝服务（DDoS）攻击检测适应性弱、计算成本高的问题,提出基于时间延迟嵌入和图卷积网络的Transformer模型（TDE-TGCN）。该模型利用Takens定理将网络流量建模为动力学系统,通过时间延迟嵌入揭示DDoS攻击对流量非线性特征的影响;采用Transfo… …   相似文献
447.
心电图作为反映心脏电活动的重要生理信号,是临床诊断心律失常等心血管疾病的重要依据。针对现有心电图分类方法在处理不同采样率数据时泛化能力不足的问题,提出了一种基于多尺度双重注意力与多模态融合的心电图分类模型。该模型引入自适应膨胀率学习机制,实现了对多种采样率心电图数据的统一建模,并… …   相似文献
448.
借助超声成像设备可以对发音生理舌体进行动态记录,超声舌成像的处理分析对于实践语言学、实验语音学研究以及语音工程应用具有重要意义,其中,舌体运动轨迹动态分析和舌体轮廓线的舌位姿态量化分析是实际应用中的关键技术。由于超声舌成像模糊、舌轮廓不清晰等噪声特性,其处理分析面临着舌位特征提取… …   相似文献
449.
Objective3D single-object tracking （3D SOT） is of paramount importance in a wide array of applications, including autonomous driving, robotics, and intelligent security. The fundamental goal of 3D SOT is to localize a specific target across a sequence of point clouds, with the only given information being its initial status. Existing matching-based 3D SOT methods generally utilize certain forms of Siamese networks for feature extraction. After transforming the cropped target template and search area embeddings to the same feature space with a shared encoder, these methods enhance target-specific features with various appearance matching techniques, such as cosine similarity and cross attention. Although the Siamese matching-based paradigm has become a popular design in existing models, appearance matching has long suffered from issues with textureless and incomplete LiDAR point clouds. Beyond this paradigm, a new motion-centric tracker, M2-Track, offers a new perspective for 3D SOT. It takes point clouds from two successive frames without cropping as input, explicitly modeling the relative target motion in a single-stream architecture, largely overcoming the challenges. However, it fuses adjacent point clouds and processes them in a single-stream architecture, lacking explicit target information from adjacent frames for accurate localization. To compensate for this deficiency, M2-Track requires additional segmentation and box refinement, which makes the training objective complex and results in cumulative errors. To this end, this study proposes a novel Siamese motion-centric tracking approach, dubbed SiamMo.MethodSiamMo adopts a simple single-stage tracking pipeline of Siamese feature extraction and motion modeling. To learn excellent features for point clouds of varying density, we first divide nonuniform points into regular voxels, which exhibit reduced sensitivity to point count variations, thus mitigating the varying sparsity issue to some extent. Afterward, we present a top-down convolutional network based on Siamese architecture to encode voxelized point clouds of successive frames into the same feature space. The network first uses sparse convolution to extract features in 3D space and then adopts dense convolution to extract features in 2D space in a bird’s eye view. In contrast with the single-steam architecture of M2-Track, Siamese architecture decouples feature extraction from temporal fusion, which enables it to extract more abundant and representative latent features while reducing information interference among successive frames. Subsequently, we design a spatiotemporal feature aggregation （STFA） module that integrates the encoded features at multiple scales for motion modeling. Intuitively, effective motion modeling necessitates rich representations at multiple scales. Integrating these multifaceted representations is expected to significantly enhance the network’s capability to accurately localize targets with various motion patterns. Moreover, we introduce a box-aware feature encoding （BFE） module that injects explicit box priors into motion features for prediction. It first encodes the bounding box size parameters of the object in the initial frame to the box-aware encoding with a multilayer perception （MLP）. Then, BFE adds the box-aware encoding and the output feature from the STFA module. Finally, the added feature is fed into an MLP to regress the relative target motion. Despite being conceptually simple, our BFE can boost tracking performance, with negligible computation. In a nutshell, using neither segmentation nor box refinement, SiamMo achieves precise localization by directly inferring the relative target motion in a single-stage manner.ResultWe compare our model with several state-of-the-art tracking methods, including Siamese matching-based and motion-centric trackers on three public datasets, namely, Kitti, NuScenes, and WOD. The quantitative evaluation metrics comprise Precision and Success. Experimental results show that our model outperforms all other methods on Kitti, NuScenes, and WOD datasets. On the Kitti dataset, compared with the second-ranked method, our method increases the average success indicator by 4.7% and the average precision indicator by 4.9%. On the NuScenes dataset, the average success indicator is increased by 14.2%, and the average precision indicator is increased by 11.5%. On the WOD dataset, the average success indicator is increased by 2.9%, and the average precision indicator is increased by 5.4%. Our method also demonstrates strong robustness to sparsity and distractors on the difficult test subsets of the Kitti and NuScenes datasets. In addition, we report the efficiency of SiamMo, which is lightweight with only 0.82 GFLOPs and 14.6 M parameters. We record the average running time of all test frames for the Car category on the Kitti dataset to evaluate the computational efficiency of our method, which achieves 108 frame/second, including 4.2 ms for pre/processing point clouds and 5.0 ms for network forward propagation on a single NVIDIA 4090 GPU. Ablation experiments conducted on the Kitti and NuScenes datasets further verify the effectiveness of the Siamese architecture, STFA module, and BFE module. In Kitti’s Car category tracking task, replacing the single-stream architecture with the Siamese architecture results in a 3.8% increase in the average success and precision. When STFA leverages features across all scales, Kitti’s Car success and precision are improved by 2.2% and 2.6%, respectively, as opposed to relying solely on single-scale features. When the BFE module is added, Kitti’s Car success and precision are improved by 2.9% and 3.6%, respectively.ConclusionIn this paper, we propose a novel and simple Siamese motion-centric tracking approach, which avoids the vulnerable appearance matching process and does not require additional presegmentation and box refinement by adopting Siamese architecture and models target motion in a simple single-stage pipeline. Comprehensive experiments demonstrate that our model surpasses state-of-the-art methods on three challenging benchmarks while demonstrating excellent robustness and maintaining a high inference speed.… …   相似文献
450.
ObjectiveAs critical components of a vibration reduction system, the primary and secondary coil springs of locomotives are vital to safe tra… …   相似文献
《中国图象图形学报》2026,31(2):499-511
451.
  
该文研究基于HowNet的知识库描述语言语法体系的术语DEF自动生成问题,提出基于最近邻提示表征的术语DEF生成模型(NNP-TDGM),将训练集中的术语DEF构造为外显记忆集,在解码器生成(首)义原或关系时,检索与待预测术语概念结构相同或相近的术语所蕴含的核心概念、重要属性和关系类型,辅助模型完成DEF的生成,解决解码器在低频样本上训练不充分的问题。另外,通过提示预训练语言模型获得术语及术语定义内蕴涵概念信息的语义表征向量,改善编码器表征能力不足的问题。经实验验证NNP-TDGM模型生成术语DEF的义原-关系-义原三元组F1值达到31.84%、关系F1值达到53.12%、义原F1值达到51.55%、首义原F1值达到68.53%,相对于基线方法分别提升了3.38%,1.45%,1.08%与0.48%。… …   相似文献
《中文信息学报》2026,40(2):133-143
452.
  
跨语言文本摘要技术通过计算机自动把一种语言的文本总结生成另一种语言摘要。现有的方法都是通过解码器的单个注意力机制来对齐两种语言的语义空间,并构造出源语言文本到目标语言摘要的映射关系。然而,单一注意力机制只是简单地匹配源语言长文本到目标语言短摘要的映射关系,这种粗糙的映射关系只能命… …   相似文献
《中文信息学报》2026,40(2):112-120
453.
  
在全球老龄化的背景下,膝关节外骨骼被广泛应用于老年人膝关节的健康维护和康复训练。膝关节外骨骼往往采用嵌入式设备进行人体下肢运动状态的识别,这需要在传感器的选择与布局、算法的准确性与计算复杂度之间找到平衡点。对此,提出了一种适用于膝关节外骨骼的人体运动识别算法。该算法利用大小腿部两… …   相似文献
454.
  
进化多任务优化是近年来计算智能领域的研究热点之一,其原理是通过任务间的知识迁移提高算法同时求解多个任务的效率。不合理的迁移知识选择会降低任务间的正向知识迁移,因此如何合理选择迁移知识成为了当前的重点研究方向。此外,在算法进化过程中,单层种群削减难以长期维持算法的高效优化性能。基于… …   相似文献
455.
  
随着物联网与5G技术的不断演进,数据流量呈现出前所未有的爆炸式增长趋势。在此背景下,传统集中式云计算模式已无法满足终端数据处理低延时和低能耗的需求,能够在数据产生源头提供即时服务的移动边缘计算(Mobile Edge Computing, MEC)逐渐成为解决该问题的最优选。计算… …   相似文献
456.
  
知识追踪(Knowledge Tracing, KT)是根据学习者在一定学习周期内的历史答题记录,构建其知识状态,并预测其未来回答问题情况。传统知识追踪研究主要以学习者行为序列为研究对象,忽略了知识之间的拓扑结构关系。近年来,基于知识静态图的知识追踪方法取得了一定进展,但未充分考… …   相似文献
457.
  
随着智慧教育的不断发展,学校可以通过检测学生课堂的专注度对学生的学习情况与教师的教学质量进行评估,从而优化教学体系。以往的研究多侧重于单模态、单角色的特征提取,但教学课堂是一个多模态、多角色且角色之间相互影响的复杂场景,因此从多模态多角色角度去探讨学生课堂的专注度具有重大意义。然… …   相似文献
458.
  
知识追踪是实现自适应学习的关键,它的目的是为了评估学生的知识状态并预测他们的未来表现。目前,数据的稀疏性问题使得现有的知识追踪模型在问题嵌入学习和学生知识状态模拟两个方面受到了限制。因此,一些研究引入了对比学习来缓解这一问题。然而,现有的对比学习方法依赖随机扰动图结构(用于问题嵌… …   相似文献
459.
  
目前,学业表现预测作为个性化教育支持系统的核心环节,已成为教育数据挖掘领域的研究热点,在教学决策优化与学生发展指导中发挥重要作用。然而,传统预测方法难以有效应对教育场景中多源异构数据的复杂关联、时序演化及群体依赖等挑战,导致预测精度与泛化能力受限。图神经网络凭借强大的关系建模与表… …   相似文献
460.
  
对于自编码器图像融合算法难以突出红外显著目标,现有融合策略难以同时考虑全局结构与局部细节信息,以及大多数融合算法过度关注统计指标,而忽视了高级视觉任务的支持需求的问题,提出了一种基于语义分割网络引导的图像融合方法,并设计了混合交叉特征机制作为融合策略。首先,在编码器和解码器之间引… …   相似文献
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