Yan Yan

TL;DR: Wang et al. as mentioned in this paper proposed a Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition, which consists of two crucial networks: a Feature decomposition Network (FDN) and a Feature Reconstruction Network (FRN).

TL;DR: A novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-labelLearning Network (MNet) and the Transfer learning Network (TNet).

TL;DR: A novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers is proposed, which improves the convergence speed of the cascaded method, which directly uses random-ferns regressors.

TL;DR: This paper proposes a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes, which achieves a good trade-off between accuracy and speed.

TL;DR: In this article, the authors proposed a rapid target detection method based on a convolutional neural network and relates to computer vision technology, which consists of the following steps: training CNN parameters by utilizing a training set, solving the problem of max-pooling losing feature by using an expander graph and generating a discriminative complete feature graph, and estimating the generalization error of the linear classifier on the discriminator by using a probable approximately correct learning framework.