This paper presents a novel generative adversarial network for the task of human pose transfer, which aims at transferring the pose of a given person to a target pose. In order to deal with pixel-to-pixel misalignment due to the pose differences, we introduce an attention mechanism and propose Pose-Guided Attention Blocks. With these blocks, the generator can learn how to transfer the details from the conditional image to the target image based on the target pose. Our network can make the target pose truly guide the transfer of features. The effectiveness of the proposed network is validated on DeepFasion and Market-1501 datasets. Compared with state-of-the-art methods, our generated images are more realistic with better facial details.
Cloud and snow detection is one of the most important tasks in remote sensing (RS) image processing areas. Distinguishing cloud and snow from RS images is a challenging task. Short-wave infrared (SWIR) band has been widely used for ice/snow detection. However, due to the lack of SWIR in high-resolution multispectral images, such as ZY-3 satellite imagery, traditional SWIR-based methods are no longer practical. In order to mitigate the adverse effects of cloud and snow detection, in this work, we propose an effective convolutional neural network (CNN) with a multilevel/ scale feature fusion module (MFFM), a channel and spatial attention module, and an encoder-decoder network structure for cloud and snow detection form ZY-3 satellite imageries. The MFFM can aggregate multiple-level/scale feature maps from the backbone network, ResNet50, for providing representative semantic feature information for cloud and snow detection. Channel and spatial attention module (CSAM) is used to further refine the semantic feature maps that outputs by MFFM thus making the network have better detection performance. The encoder-decoder structure allows the proposed CNN to restore detailed object boundaries thus making the detection results more accuracy. Experimental results on the ZY-3 satellite imageries dataset demonstrate that the proposed network can accurately detect cloud and snow, and outperforms several state-of-the-art methods.