Titlebook: Web and Big Data; Third International Jie Shao,Man Lung Yiu,Bin Cui Conference proceedings 2019 Springer Nature Switzerland AG 2019 artifi

Braggart

书目名称Web and Big Data影响因子(影响力)




书目名称Web and Big Data影响因子(影响力)学科排名




书目名称Web and Big Data网络公开度




书目名称Web and Big Data网络公开度学科排名




书目名称Web and Big Data被引频次




书目名称Web and Big Data被引频次学科排名




书目名称Web and Big Data年度引用




书目名称Web and Big Data年度引用学科排名




书目名称Web and Big Data读者反馈




书目名称Web and Big Data读者反馈学科排名

Indelible

愤愤不平

PANT

Yunpei Zheng,Lin Li,Jianwei Zhang,Qing Xie,Luo Zhongdevelop the subject in a way which would have been impossible otherwise. It is a rapidly growing research field which began to flourish in the 1960s and which is beginning to produce commercial devices. Significant developments have been made, both in the theory and practical engineering of the subj

大炮

Zheng Wang,Cai Na,Zeyu Ma,Si Chen,Lingyun Song,Yang Yangods is that they focus on salient region mining and feature extraction, while ignoring the ability to incorporate discriminative and complementary features from other parts of the image. In order to address this issue, we introduce a novel network known as the Discriminative Region Enhancing and Sup

disciplined

daredevil

Infect

Huanran Xue,Jiawei Jiang,Yingxia Shao,Bin Cui the bias of a classifier is a direct consequence of a bias in the training set, frequently caused by the co-occurrence of relevant features and irrelevant ones. To mitigate this issue, we require learning algorithms that prevent the propagation of known bias from the dataset into the classifier. We

LINE

Xiyao Fu,Yi Bin,Zheng Wang,Qin Wei,Si Chenit highly class-imbalanced distributions, yielding two main challenges: relative imbalance amongst the classes and data scarcity for mediumshot or fewshot classes. In this work, we address the problem of long-tailed recognition wherein the training set is highly imbalanced and the test set is kept b

彩色