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MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation

Autor: Yuan, Yanli, Wang, Bingbing, Zhang, Chuan, Xu, Jingyi, Liu, Ximeng, Zhu, Liehuang

Segmentation of organs of interest in medical CT images is beneficial for diagnosis of diseases. Though recent methods based on Fully Convolutional Neural Networks (F-CNNs) have shown success in many segmentation tasks, fusing features from images wi

Externí odkaz: http://arxiv.org/abs/2405.04064

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Agent-driven Generative Semantic Communication with Cross-Modality and Prediction

Autor: Yang, Wanting, Xiong, Zehui, Yuan, Yanli, Jiang, Wenchao, Quek, Tony Q. S., Debbah, Merouane

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To

Externí odkaz: http://arxiv.org/abs/2404.06997

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Semantic Change Driven Generative Semantic Communication Framework

Autor: Yang, Wanting, Xiong, Zehui, Du, Hongyang, Yuan, Yanli, Quek, Tony Q. S.

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature

Externí odkaz: http://arxiv.org/abs/2309.12775

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Joint Network Topology Inference via Structured Fusion Regularization

Autor: Yuan, Yanli, Soh, De Wen, Yang, Xiao, Guo, Kun, Quek, Tony Q. S.

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component shared among

Externí odkaz: http://arxiv.org/abs/2103.03471

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