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Road detection plays a critical role in the application of smart transportation. The performance of the mainstream methods such as PSPNet (Pyramid Scene Parsing Network), DeepLab V3, FCRN(Fully Convolutional Residual Networks) still suffers from uncertain disturbances of surface abrasion buildings, pedestrians, and variation of illumination like tree-shadow. The extracted features are vulnerable to extra-disturbance, and non-local spatial-context information has not been fully utilized. In this paper, a detector based on anti-disturbance and variable-scale spatial context features (AVD) is proposed: the training of the multi-layer features of the detector is always taken under the imposing of fake-featuredisturbance from an independent generator, which is trained to exacerbate the detector errors and the mistakes of feature discriminator. The detector is prepared to be immune from the fake-feature-disturbance, and the discriminator is trained to distinguish the differences between the non-interference features and disturbing features. We also designed a novel variable-scale spatial context module to enhance the richness performance of the extracted features. And a soft connection link is bridged between the low and high feature layers. The detection experiments on the Munich road dataset and urban road dataset show that AVD is better than all the mainstream above methods. Our method increases the accuracy by 3% on the Munich remote sensing dataset and 0.4% on the urban road dataset. Our code and datasets are available for download.

The study of face frontalization is essential for improving face recognition accuracy in extreme pose scenarios. Mainstream methods like TP-GAN, CAPG-GAN, etc., have made meaningful contributions. However, they still suffer from two problems: the lack of extracted feature diversity and the blurred details in generated images. This paper proposes a pre-trained feature fusion and multi-domain identification generative adversarial network (PM-GAN) for face frontalization: the features of the model pre-trained on large-scale datasets are fused with the original features of the encoder to enhance the diversity and robustness of features. In order to fuse features more effectively, we designed a novel feature fusion module (FFM). In addition, a group of global and local discriminators is introduced to reinforce the local details and realism of generated frontal faces. Experimental results show that our proposed method outperforms state-of-the-art methods on M 2 FPA and CAS-PEAL datasets.

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