Titlebook: Advances in Visual Computing; 15th International S George Bebis,Zhaozheng Yin,George Baciu Conference proceedings 2020 Springer Nature Swit

incompatible

書(shū)目名稱Advances in Visual Computing影響因子(影響力)




書(shū)目名稱Advances in Visual Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱Advances in Visual Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Advances in Visual Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Advances in Visual Computing被引頻次




書(shū)目名稱Advances in Visual Computing被引頻次學(xué)科排名




書(shū)目名稱Advances in Visual Computing年度引用




書(shū)目名稱Advances in Visual Computing年度引用學(xué)科排名




書(shū)目名稱Advances in Visual Computing讀者反饋




書(shū)目名稱Advances in Visual Computing讀者反饋學(xué)科排名

時(shí)代

Self-Competitive Neural NetworksNN tries to improve its accuracy by competing with itself (generating hard samples and then learning them), the technique is called Self-Competitive Neural Network (SCNN). To generate such samples, we pose the problem as an optimization task, where the network weights are fixed and use a gradient de

增長(zhǎng)

NEG

consent

prediabetes

缺陷

Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3chnique that finds individual and unbounded dropout rates for each neural network weight. Experiments on the PASCAL VOC benchmark dataset show promising results where variational dropout combined with the most efficient YOLOv3 variant lead to an extremely sparse solution that reduces 95% of the base

凝視

AER

Towards Optimal Ship Navigation Using Image Processingh has some practical and computational limitations; however, the future unmanned vessels could benefit from the improved applications of this route optimization approach in terms of energy consumption, time, and workforce.

錫箔紙