Interactive segmentation is a technique for picking objects of interest in images according to users’ input interactions. Some recent works take the interactive guide deep neural network training, where click information utilized as weak-supervised information. However, limited by learning capability model, this structure does not accurately represent user’s interaction intention. In work, we p...

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance learning speed due hard labels non-uniform distribution over labels. Therefore, this paper proposed an idea enhance CNN HSIC using soft that a weighted av...

Recent image style transfer methods use a pre-trained convolutional neural network as their feature encoder. However, the is not optimal for but rather classification. Furthermore, they require time-consuming alignment to consider existing correlation among channels of encoded map. In this paper, we propose an end-to-end learning method that optimizes both encoder and decoder networks task reli...