Abstract: Object detection (OD) in unmanned aerial vehicle (UAV) images faces many challenges, with diverse-scale objects and small objects being particularly prominent issues. To alleviate these ...
Abstract: LiDAR semantic segmentation is essential in autonomous vehicle safety. A rotating 3D LiDAR projects more laser points onto nearby objects and fewer points onto farther objects. Therefore, ...
Abstract: This paper proposes a novel real-time semantic segmentation network via frequency domain learning, called FDLNet, which revisits the segmentation task from two critical perspectives: spatial ...
Abstract: Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples. Yet ...
Abstract: Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving ...
Abstract: We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. PC-ECGNN leverages interference link distances ...
Abstract: It has long been recognized that the standard convolution is not rotation equivariant and thus not appropriate for downside fisheye images which are rotationally symmetric. This paper ...
Abstract: Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional ...
Abstract: The serious concerns over the negative impacts of Deepfakes have attracted wide attentions in the community of multimedia forensics. The existing detection works achieve deepfake detection ...
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