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机译:通过堆叠的AutoEncoder的融合和自培训CNNS的深度合理检测
Texas A&M Univ Dept Elect & Comp Engn Uvalde TX 77843 USA|Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan;
Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan|Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan;
Natl Chiaorung Univ Dept Comp Sci Hsinchu 300 Taiwan;
Texas A&M Univ Dept Elect & Comp Engn Uvalde TX 77843 USA;
Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan|Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 106 Taiwan;
Proposals; Saliency detection; Image segmentation; Image reconstruction; Reliability; Task analysis; Fuses; Co-saliency detection; stacked autoencoder; reconstruction residual; adaptive fusion; optimization; self-paced learning; CNNs;
机译:基于雾环境中烟雾检测的基于深度融合CNN的高效关注
机译:低秩多尺度融合的图像显着性和共显着性检测
机译:通过多个内核增强和融合进行RGBD共显着性检测
机译:压缩域中学习双流融合CNN的深度视频帧插值检测
机译:基于融合的深层CNN,适用于综合汽车。
机译:使用更快的R-CNN和深CNN在乳腺癌组织病理学图像中基于人工智能的有丝分裂检测
机译:压缩域中学习双流融合CNN的深度视频帧插值检测