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Accurately identifying priority attention regions in complex driving scenarios is critical yet challenging, due to the coupling of heterogeneous factors such as motion, structured semantics, and human
Few-shot defect segmentation (FSDS) has emerged as an effective approach to address the challenge of limited annotated samples in industrial quality inspection. However, existing methods often rely on
Person re-identification (Re-ID), as a core technology for target tracking and identity authentication, is widely applied in intelligent surveillance and security systems. In recent years, radio frequ
Android malware presents a persistent threat to users’ privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detection (AMD) methods.
In this work, we investigate the task of Federated Generalized Category Discovery (Fed-GCD), which aims to leverage labeled data from known classes to cluster unlabeled samples from both known and unk
Indoor presence detection is essential for security monitoring in smart spaces, supporting applications such as access control and anomaly detection. While Wi-Fi Channel State Information (CSI) sensin
As textual adversarial attacks pose risks to natural language processing models, adversarial training is often utilized to improve robustness of these systems. In a strict black-box scenario, most que
This paper investigates the distributed generalized Nash equilibrium (GNE) seeking problem for purely monotone games with set and shared affine inequality constraints over communication-constrained ne
Current research on Audio Deepfake Detection (ADD) mainly relies on centralized training paradigms, while paying limited attention to privacy preservation under data localization regulations or user-c
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The IEEE Signal Processing Society is dedicated to supporting the professional growth and career advancement of its members in the dynamic field of signal processing. Learn More
Signal processing education and professional development program for all career levels. Learn more.