Výsledky vyhledávání - "Wang Mingqing"

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ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception

Autor: Wang, Mingqing, Nie, Zhiwei, He, Yonghong, Ren, Zhixiang

Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks. Protein domains are "building blo

Externí odkaz: http://arxiv.org/abs/2405.15158

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Point Resampling and Ray Transformation Aid to Editable NeRF Models

Autor: Li, Zhenyang, Chen, Zilong, Qu, Feifan, Wang, Mingqing, Zhao, Yizhou, Zhang, Kai, Peng, Yifan

In NeRF-aided editing tasks, object movement presents difficulties in supervision generation due to the introduction of variability in object positions. Moreover, the removal operations of certain scene objects often lead to empty regions, presenting

Externí odkaz: http://arxiv.org/abs/2405.07306

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Intelligent model for offshore China sea fog forecasting

Autor: Xiang, Yanfei, Zhang, Qinghong, Wang, Mingqing, Xia, Ruixue, Kong, Yang, Huang, Xiaomeng

Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods a

Externí odkaz: http://arxiv.org/abs/2307.10580

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Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers

Autor: Wang, Mingqing, Li, Jiawei, Li, Zhenyang, Luo, Chengxiao, Chen, Bin, Xia, Shu-Tao, Wang, Zhi

Unsupervised anomaly detection (UAD) has been widely implemented in industrial and medical applications, which reduces the cost of manual annotation and improves efficiency in disease diagnosis. Recently, deep auto-encoder with its variants has demon

Externí odkaz: http://arxiv.org/abs/2303.17505

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Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification

Autor: Yu, ChengHui, Tang, MingKang, Yang, ShengGe, Wang, MingQing, Xu, Zhe, Yan, JiangPeng, Chen, HanMo, Yang, Yu, Zeng, Xiao-Jun, Li, Xiu

Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair an

Externí odkaz: http://arxiv.org/abs/2207.07303

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