Few-shot Font Generation (FFG) is a practical technology widely used in designing artistic characters, handwriting imitation, and identification, etc., which aims to generate realistic font images with a few reference...
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In the field of open-set recognition, conventional models often focus on addressing challenges within a single hierarchical category, and these methods frequently lack inter-pretability. In this paper, we propose a no...
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Infrared small target detection (IRSTD) is a technique that has been developed to detect small and faint targets in cluttered backgrounds. It shows extensive use in medical diagnosis and aircraft navigation. However, ...
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In the field of open-set recognition, conventional models often focus on addressing challenges within a single hierarchical category, and these methods frequently lack inter-pretability. In this paper, we propose a no...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
In the field of open-set recognition, conventional models often focus on addressing challenges within a single hierarchical category, and these methods frequently lack inter-pretability. In this paper, we propose a novel solution that utilizes attributes and hierarchical relationships to achieve interpretable open-set recognition. Our method is centered around the visual-semantic attribute space. By leveraging hierarchy division, we can decompose the attributes into more granular components, thereby yielding additional performance improvements. When confronted with an unfamiliar object, our method not only classifies it as an unknown category but also provides insights into the broader category and its associated attributes. This capability enhances interpretability by offering valuable information regarding the potential category and characteristics of the object. Experimental results demonstrate great performance improvements compared to existing methods.
multiple-input-multiple-output (MIMO) based on energy detection has the advantages of being insensitive to Doppler shift and low complexity. However, the square-law processing causes the nonlinear mutual interference ...
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The two key steps in the space-based optical passive tracking system are track association and fusion. The traditional distributed fusion methods are based on three-dimensional trajectory and are not suitable for the ...
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Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavi...
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The landmark detection has been widely investigated for the human pose with rapid progress in recent years. In this work, we aim at dealing with a new problem: aircraft landmark detection in the wild. We have a key ob...
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Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficien...
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While encrypted traffic improves security, it is also used by attackers to hide the transmission content to evade detection. Currently, traffic side-channel features combined with Deep Learning (DL) are widely used fo...
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ISBN:
(数字)9798331506209
ISBN:
(纸本)9798331506216
While encrypted traffic improves security, it is also used by attackers to hide the transmission content to evade detection. Currently, traffic side-channel features combined with Deep Learning (DL) are widely used for malicious traffic classification, but traditional DL-based methods require large training samples and struggle with new threats. Prototypical networks in meta-learning have been effective in few-shot malicious traffic classification. However, existing methods face challenges such as "overlooking hierarchical traffic dependencies" and "bias in class prototype generation". The former means that the existing methods lack the representation design based on the hierarchical structure of traffic, resulting in insufficient feature extraction, and the latter indicates that the existing methods struggle to capture the diverse distribution of traffic features, resulting in unstable classification performance. To address the above problems, this paper proposes a few-shot encrypted malicious traffic classification method based on Hierarchical Semantics and Adaptive Prototype Learning Network (HANet). First, network traffic’s fine-grained features are represented in a multi-level matrix, with a hierarchical network structure designed to extract features comprehensively. Then, class prototypes are dynamically generated using a neighborhood partitioning method to balance simple and complex traffic feature distributions, enhancing generalization. Experiments on the CICandMal2017 dataset show that HANet offers significant performance over other few-shot malicious traffic classification methods. HANet has achieved a classification accuracy of more than 80% with only 5, 10, and 15 lab.led traffic samples, realizing effective detection of few-shot encrypted malicious traffic.
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