Andrew's Sine Estimator (ASE) has recently been used to invent adaptive filtering, which can combat more kind of noises than conventional estimators. Inspired by the LMS and its sparse forms, normalization and pro...
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In the down-link secure space-air-ground integrated network (SAGIN) with practical discrete modulation symbols, we consider the joint design of the power of satellite and unmanned aerial vehicle (UAV) swarm beamformin...
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Cloud-based expression recognition from high-resolution facial images may put the subjects’ privacy at risk. We identify two kinds of privacy leakage, the appearance leakage in which the visual appearances of subject...
Cloud-based expression recognition from high-resolution facial images may put the subjects’ privacy at risk. We identify two kinds of privacy leakage, the appearance leakage in which the visual appearances of subjects are disclosed and the identity-pattern leakage in which the identity information of subjects is dug out. To address both leakages, we propose privacy-protected facial expression recognition from low-resolution facial images with the help of high-resolution facial images. Specifically, to prevent appearance leakage, we propose to extract identity-invariant representations from downsampled images, from which the visually distinguishable appearances cannot be recovered. To prevent identity-pattern leakage, we propose to eliminate the identity information from the extracted representations by leveraging the disentangled representations of high-resolution images as privileged information. After training, our method can fully capture identity-invariant representations from downsampled images for expression recognition without the requirement of high-resolution samples. These privacy-protected representations can be safely transmitted through the Internet. Experimental results in different scenarios demonstrate that the proposed method protects privacy without significantly inhibiting facial expression recognition.
A low coupling sparse array (LCSA) is presented, analyzed and discussed on the basis of the recent proposed uniform linear array (ULA) fitting scheme with close-expressions, and its performance with different uniform ...
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Autonomous driving relies on multiple sensors, such as lidar and cameras, to perceive the surrounding environment and the vehicle’s own position. Among them, lidar point cloud segmentation is a crucial and challengin...
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Current works on multimodal facial expression recognition typically require paired visible and thermal facial images. Although visible cameras are readily available in our daily life, thermal cameras are expensive and...
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3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with ...
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Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph le...
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Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model ...
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Geo-replication is essential for providing low latency response and quality Internet services. However, designing fast and correct geo-replicated services is challenging due to the complex trade-off between performanc...
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