Due to data imbalance, existing spammer group detection methods often yield suboptimal performance. Moreover, many of these approaches operate as black boxes, offering little to no interpretability for their detection...
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At present, the topic model based on LDA as an information recommendation method has the defect of neglecting the emotional information in social platform. Therefore, a microblog user recommendation strategy I-TES (in...
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The mining of software community structure is of great significance in identifying software design pattern, software maintenance, software security and optimizing software structure. To improve the accuracy of descrip...
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With the rapid development of computing infrastructure and the increasing demand for big data processing, object counting has emerged as a critical and challenging task. Few-Shot Object Counting (FSOC) aims to estimat...
With the rapid development of computing infrastructure and the increasing demand for big data processing, object counting has emerged as a critical and challenging task. Few-Shot Object Counting (FSOC) aims to estimate the number of objects in any category based on a few visual exemplar prompts. Existing methods typically rely on bounding boxes to guide the model in understanding the correlation between visual exemplars and the query image, followed by regressing a density map for counting. However, despite the growing overall average performance, we contend that the exploration of more generic counting frameworks has not received adequate attention. In this work, we propose a novel Point-guided Exemplar Prompting Network (PEPNet), a new framework that uses point annotations as prompts to guide object counting. PEPNet consists of two core components: a Multi-scale Attention Fusion Module (MAFM) and an Iterative Encoding Matching Module (IEMM). MAFM integrates spatial and channel attention mechanisms to adaptively highlight critical regions while capturing multi-scale features, effectively balancing global context and local details. IEMM, for the first time, employs a point-guided prompting strategy to iteratively encode visual exemplars, suppressing irrelevant features and enhancing important ones. In particular, the multi-head similarity matching block in IEMM refines the matching process progressively, improving the correlation between exemplars and the query image, thereby boosting object recognition and counting accuracy. Extensive experiments on multiple benchmark datasets, including FSC-147, Val-COCO, Test-COCO, CARPK, and ShanghaiTech, demonstrate the effectiveness of PEPNet. Notably, on the FSC-147 validation set, our method achieves a performance improvement of 1.9% in Mean Absolute Error (MAE) and 12.3% in Root Mean Square Error (RMSE) compared to the state-of-the-art SPDCN. Additionally, on the test set, we observe performance improvements of 0.2% in MAE
In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves th...
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With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influ...
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Multi-access Edge Computing (MEC) has been a promising solution that enables Internet of Things (IoT) devices to support computation-intensive applications by offloading some tasks to the network edge. However, most e...
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Currently, many studies use Fourier amplitude spectra of speech signals to predict depression levels. However, those works often treat Fourier amplitude spectra as images or sequences to capture depression cues using ...
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With the widespread use of touch-screen devices, it is more and more convenient for people to draw sketches on screen. This results in the demand for automatically understanding the sketches. Thus, the sketch recognit...
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