Titlebook: Industrial Democracy in the Chinese Aerospace Industry; The Innovation Catal Denise Tsang Book 2017 The Editor(s) (if applicable) and The A

骗子

狂热语言

Denise Tsangm, some image related side information, such as captions and tags, often reveal underlying relationships across images. In this paper, we present an efficient weakly-supervised learning by using a Side Information Network (SINet), which aims to effectively carry out a large scale classification with

Custodian

Denise Tsangssive performance of bilinear pooling. The standard matrix normalization, however, needs singular value decomposition (SVD), which is not well suited in the GPU platform, limiting its efficiency in training and inference. To resolve this issue, the Newton-Schulz (NS) iteration method has been propos

Senescent

Denise Tsangssification networks are often not accurate due to the lack of fine pixel-level supervision. In this paper, we propose to leverage pixel-level similarities across different objects for learning more accurate object locations in a complementary way. Particularly, two kinds of constraints are proposed

GEON

originality

Denise Tsangs shown its effectiveness in accelerating the model training speed and improving model generalization capability. The success of BN has been explained from different views, such as reducing internal covariate shift, allowing the use of large learning rate, smoothing optimization landscape, etc. To m

向下五度才偏

Denise Tsang bring out an emerging research topic of face forgery detection. However, it is extremely challenging since recent advances are able to forge faces beyond the perception ability of human eyes, especially in compressed images and videos. We find that mining forgery patterns with the awareness of freq