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考虑到软件需求文本区别于其他普通文本的独特领域信息外, 还包含一些重要的上下文关系以及固有的二义性问题, 本文提出了一个图卷积与BERT融合的软件需求自动分类模型——BERT-FGCN (BERT-FusionGCN), 将图卷积网络(GCN)用于软件需求分类领域, 利用GCN对邻居节点信息进行信息传播和特征聚合的优势, 捕捉需求语句中单词或句子之间的上下文关系, 以进一步提高需求分类的结果. 首先构建需求文本的文本共现图和依存句法图, 将两种图进行融合来捕获句子的结构信息, 利用GCN对建模后的需求语句的图结构进行卷积得到图向量, 最后将图向量与BERT特征提取后得到的向量进行融合, 以此来对软件需求文本自动分类. 在PROMISE数据集上进行实验, BERT-FGCN在二分类上的F1分数达到95%, 多分类任务的F1分数提高2%.… … 相似文献
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针对道路损伤检测面临的多尺度目标、复杂的目标结构、样本分布不均及难易样本对边界框回归的影响等问题, 本研究提出了一种基于改进YOLOv8的道路损伤检测算法. 该方法通过引入动态蛇形卷积 (dynamic snake convolution, DSConv) 替代原有C2f (faster implementation of CSP bottleneck with 2 convolutions) 模块中的部分Conv, 以自适应聚焦于细小而曲折的局部特征, 增强对几何结构的感知. 在每个检测头前引入高效多尺度注意力 (efficient multi-scale attention, EMA) 模块, 实现跨维度交互, 捕获像素级别关系, 提升对复杂全局特征的泛化能力. 同时, 增设小目标检测层以提高小目标检测精度. 最后, 提出Flex-PIoUv2策略, 通过线性区间映射和尺寸适应性惩罚因子, 有效缓解样本分布不均和锚框膨胀问题. 实验结果表明, 该改进模型在RDD2022数据集上的F1分数、平均精度均值 (mAP50、mAP50-95) 分别提高了1.5百分点、2.1百分点和1.2百分点. 此外, 在GRDDC2020和China road damage数据集上的验证结果显示, 该算法具有良好的泛化性.… … 相似文献
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针对现有故障根因分析方法因果关系丢失、在复杂环境中分析效率低下以及缺乏对于非机器指标故障类型分析能力的问题,提出一种基于因果干预的微服务系统故障根因分析（CIMF-RCA）方法。首先,利用马尔可夫假设和调用模式对调用链和微服务进行筛选,从而缩减干预识别的搜索空间并提高故障根因分析… … 相似文献
丁建立;何雨峰;王静 《计算机应用》2025,(1):196-203
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In response to the need for sentiment classification analysis across various indices in automobile reviews,this study introduces two tasks:i… … 相似文献
《小型微型计算机系统》2025,46(1):135-142
4850.
针对零样本动作识别（ZSAR）算法的框架缺乏结构性指导的问题,以基于能量的模型（EBM）指导框架设计,提出基于注意力机制和能量函数的动作识别算法（ARAAE）。首先,为了得到EBM的输入,设计了光流加3D卷积（C3D）架构的组合以提取视觉特征,从而达到空间去冗余的效果;其次,将视… … 相似文献
王丽芳;吴荆双;尹鹏亮;胡立华 《计算机应用》2025,(1):234-239
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The problem of incremental attribute reduction for incomplete hybrid decision systems has been a hot topic of research in recent years.A def… … 相似文献
《小型微型计算机系统》2025,46(1):128-134
4853.
在商品卖点生成中,吸引人的卖点与用户的需求密切相关。电商平台上用户产生的问答数据直接反映了用户最关注的内容,所以该文尝试基于此问答讨论生成商品卖点。该生成任务的挑战是：(1)没有相关的研究数据集;(2)问答对内和对间的依赖关系复杂;(2)卖点包含的关键信息分散在多个问答对中。为了… … 相似文献
申磊;占浩澜;张海楠;陈宏申;赵晓芳 《中文信息学报》2025,39(1):144-152
4854.
中文命名实体识别（NER）任务旨在抽取非结构化文本中包含的实体并给它们分配预定义的实体类别。针对大多数中文NER方法在上下文信息缺乏时的语义学习不足问题;提出一种层次融合多元知识的NER框架——HTLR （Chinese NER method based on Hierarchi… … 相似文献
吕学强;王涛;游新冬;徐戈 《计算机应用》2025,45(1):40-47
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ObjectiveAs a basic branch of computer vision, object detection plays an important role in subsequent tasks such as image segmentation and object tracking. It aims to find all the objects in the image and determine the location and category of the objects. It is used in industrial testing and has profound and extensive applications in aerospace, autonomous driving, and other fields. Aircraft detection in remote sensing images is of great significance to both military and civilian fields such as air traffic control and battlefield dynamic monitoring. As a result of the large differences in object size in remote sensing aircraft images, the acquisition process is affected by factors such as lighting and occlusion, resulting in similar characteristics of different types of aircraft, poor detection of small objects, and the inability to achieve fine-grained distinction within categories. In object detection, the loss function is used to measure the difference between the model prediction and the actual object, which directly affects the performance and convergence speed of the model. Adjusting the model parameters so that the value of the loss function reaches the minimum value can improve the accuracy of the model in the test set. The loss function of YOLOv5 consists of position loss, category loss and confidence loss. YOLOv5 uses the intersection over union （IoU） and the derivative algorithm complete IoU by default, and provides IoU, generalized IoU, and distance IoU for replacement. However, for small object detection, especially with anchor box-based algorithms such as YOLOv5, the IoU series indicators cannot meet application needs well. Different types of remote sensing aircraft have fine-grained characteristics, which are reflected in subtle differences between classes, large differences within classes, and detail accuracy within classes. For fine-grained recognition tasks, extracting local information is crucial. The feature fusion module PANet used by YOLOv5s cannot achieve global feature fusion and is not conducive to extracting fine-grained features. To solve the above problems, this article proposes a model improvement algorithm based on YOLOv5s.MethodIn view of the shortcomings of IoU in small object detection based on YOLOv5, this article introduces Gaussian Wasserstein distance into the calculation of bounding box overlap to improve the detection performance of the network. Different from the IoU series of algorithms that calculate the similarity between different prediction boxes and real boxes based on the set of pixels contained in the bounding box, the Gaussian Wasserstein distance abandons the set, models the bounding box as a two-dimensional Gaussian distribution, and proposes a new metric called normalized Gaussian Wasserstein distance to calculate the similarity between frames, which fundamentally solves the problem of IoU in small object detection based on YOLOv5. In response to PANet’s shortcomings in fine-grained detection, this article introduces the gather-and-distribute feature aggregation module in Gold-YOLO into YOLOv5s to enhance the YOLOv5s network’s ability to extract fine-grained features through convolution and self-attention mechanisms. 1） The method combining Gaussian Wasserstein distance and traditional IoU is used to improve the loss function of YOLOv5s. 2） The gather-and-distribute feature aggregation module is introduced in the neck part of YOLOv5s to enhance the network’s local feature extraction capabilities. Through the above two methods, the overall detection accuracy is improved. To test the advantages of this algorithm in fine-grained and small object recognition on military aircraft, this paper uses the remote sensing aircraft fine-grained classification dataset MAR20 and the remote sensing aircraft small object dataset CORS-ADD to conduct experiments. In the field of remote sensing military aircraft identification, different types of aircraft often have similar characteristics, resulting in different types of aircraft having similar characteristics, making it difficult to achieve intra-class identification. This article uses the open-source object detection remote sensing image dataset military aircraft recognition 20（MAR20） to achieve fine-grained recognition of remote sensing military aircraft. The dataset contains a total of 3 842 images, including 20 military aircraft models （SU-35, C-130, C-17, C-5, F-16, TU-160, E-3, B-52, P-3C, B-1B, E-8, TU-22, F-15, KC-135, F-22, FA-18, TU-95, KC-10, SU-34, SU-24）. The CORS-ADD dataset is a complex optical remote sensing aircraft small object dataset that is manually annotated and constructed by the Space Optical Engineering Research Center of Harbin Institute of Technology. It contains a total of 7 337 images, including 32 285 aircraft instances, and the object size ranges from 4 × 4 pixels to 240 × 240 pixels. Different from the single data source of previous remote sensing datasets, the CORS-ADD dataset comes from satellite platforms such as Google Maps, WorldView-2, WorldView-3, Pleiades, Jilin-1, and IKONOS, covering airports, aircraft carriers, oceans, land, and other scenarios, as well as aircraft objects such as bombers, fighter jets, and early-warning aircraft at typical airports in China and the United States.ResultTo test the algorithm improvement effect of the two improved modules on remote sensing aircraft recognition based on YOLOv5s, this article compares the model performance of the original YOLOv5s with the introduction of normalized Gaussian Wasserstein distance（NWD） （r is the weight parameter used to adjust the ratio of IoU and NWD） and GD. The experimental result shows that the introduction of NWD and GD can improve the recognition accuracy to varying degrees, and the improvements are effective. When the ratio of IoU to NWD is 1：1, the recognition effect of the MAR20 dataset is the best; when the ratio of IoU to NWD is 1：9, the recognition effect of the CORS-ADD dataset is the best. Experimental results show the following： For the MAR20 dataset, compared with that of YOLOv5s, YOLOv8s, and Gold-YOLO, the mAP of improved YOLOv5s increased by 1.1%, 0.7% and 1.8% respectively; for the CORS-ADD dataset, mAP increased by 0.6%, 1.7%, and 3.9%, respectively.ConclusionAn improved YOLOv5s network is proposed to solve the problems of large object size differences and high intra-class similarity in the process of remote sensing aircraft image recognition. On the basis of YOLOv5s, the loss function of YOLOv5s is improved by combining the Gaussian Wasserstein distance with the traditional IoU metric, which improves the detection effect of objects of different sizes, thereby improving the detection accuracy of the model. At the same time, to solve the problem of the characteristics of different types of aircraft being similar and the difficulty of distinguishing between sub-categories, this article uses the gather-and-distribute feature aggregation module in Gold-YOLO to enhance the ability of the YOLOv5s network to extract fine-grained features. A comparison shows that the improved YOLOv5s has a better model detection accuracy than that of YOLOv5s, YOLOv8s, Gold-YOLO, and Faster R-CNN. To improve the image processing speed of the model without reducing the accuracy of the model and to reduce the consumption of computing resources as much as possible to achieve lightweight deployment in the future, this article will consider using the C3_DSConv network to replace the C3 network of the YOLOv5s detection part to improve the model check speed and make it lightweight.… … 相似文献
《中国图象图形学报》2025,30(1):282-296
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ObjectiveThree-dimensional reconstruction is a critical technology in the field of computer vision, with profound implications across divers… … 相似文献
《中国图象图形学报》2025,30(1):225-239
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ObjectiveImage stitching, a cornerstone in the field of computer vision, is dedicated to assembling a comprehensive field-of-view image by m… … 相似文献
《中国图象图形学报》2025,30(1):173-187
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Since the inception of the marine power strategy, there has been an increasing focus on an investigation into the quality of underwater imag… … 相似文献
《中国图象图形学报》2025,30(1):51-65
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网站版权所有：中国科学院软件研究所 京ICP备09084417号-9
地址：北京海淀区中关村南四大街4号 邮编：100190 电话：010-62562563 E-mail ： cj_mail@jscas.ac.cn
本系统由:北京勤云科技发展有限公司提供技术支持
