Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term hea...
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software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems. In existing code-visualization-based defect prediction methods, challenges arise fro...
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This study proposes a bearing fault diagnosis method that combines the Cuckoo Optimization Algorithm (COA) with the KAN algorithm. COA, as an intelligent optimization algorithm, is primarily used to find the optimal h...
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Fundus image is vital for the diagnosis and monitoring of various eye diseases, where the accuracy of diagnostic results is largely determined by the quality of the obtained images. However, the performance of existin...
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In this report, we present a pre-organization summary of the fifth workshop on emerging softwareengineering education (WESEE) to be held on February 23, 2023, co-located with the 16th Innovations in software Engineer...
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Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attir...
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The pivotal role of white-box testing with respect to software quality assurance, necessitates dissemination of education materials related to white-box testing in the course curriculum. In this poster, we describe ou...
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In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and *** understand the scenes and activities from human life logs,human-object interaction(HOI)is...
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In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and *** understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose *** understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been *** existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized *** existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the *** estimation depends on appearance features and spatial ***,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the ***,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using *** predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction *** interactions are linked with the human and object to predict the *** experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.
作者：
Ma, XinsongZou, XinLiu, WeiweiSchool of Computer Science
National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China
Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigati...
Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigating the decision rule based on the proposed score function. Different from previous work, this paper aims to design a decision rule with rigorous theoretical guarantee and well empirical performance. Specifically, we provide a new insight for the OOD detection task from a hypothesis testing perspective and propose a novel generalized Benjamini Hochberg (g-BH) procedure with empirical p-values to solve the testing problem. Theoretically, the g-BH procedure controls false discovery rate (FDR) at pre-specified level. Furthermore, we derive an upper bound of the expectation of false positive rate (FPR) for the g-BH procedure based on the tailed generalized Gaussian distribution family, indicating that the FPR of g-BH procedure converges to zero in probability. Finally, the extensive experimental results verify the superiority of g-BH procedure over the traditional threshold-based decision rule on several OOD detection benchmarks. Copyright 2024 by the author(s)
Automatic modulation recognition-oriented Deep Neural Networks (ADNNs) have achieved higher recognition accuracy than traditional methods with less labor overhead. However, their high computation complexity usually fa...
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