Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism. However, few works have focused on harvesting boundary information to improve the segmentation performance. In order to enhance the feature similarity within the object and keep discrimination from other objects, we propose a boundary-aware graph convolution (BGC) module to propagate features within the object. The graph reasoning is performed among pixels of the same object apart from the boundary pixels. Based on the proposed BGC module, we further introduce the Boundary-aware Graph Convolution Network(BGCNet), which consists of two main components including a basic segmentation network and the BGC module, forming a coarse-to-fine paradigm. Specifically, the BGC module takes the coarse segmentation feature map as node features and boundary prediction to guide graph construction. After graph convolution, the reasoned feature and the input feature are fused together to get the refined feature, producing the refined segmentation result. We conduct extensive experiments on three popular semantic segmentation benchmarks including Cityscapes, PASCAL VOC 2012 and COCO Stuff, and achieve state-of-the-art performance on all three benchmarks.

In this paper, we propose a graph-based feature aggregation network (G-FAN) for video face recognition. Compared with the still image, video face recognition exhibits great challenges due to huge intra-class variability and high inter-class ambiguity. To address this problem, our G-FAN first uses a Convolutional Neural Network to extract deep features for every input face of a subject. Then, we build an affinity graph based on the relation between facial features and apply Graph Convolutional Network to generate fine-grained quality vectors for each frame. Finally, the features among multiple frames are adaptively aggregated into a discriminative vector to represent a video face. Different from previous works that take a single image as input, our G-FAN could utilize the correlation information between image pairs and aggregate a template of faces simultaneously. The experiments on video face recognition benchmarks, including YTF, IJB-A, and IJB-C show that: (i) G-FAN automatically learns to advocate high-quality frames while repelling low-quality ones. (ii) G-FAN significantly boosts recognition accuracy and outperforms other state-of-the-art aggregation methods.

This paper presents a novel method based on the generative adversarial network for radial distortion correction. Instead of generating a corrected image, our generator predicts a pixel flow map to measure the pixel offset between the distorted and corrected image. The quality of the generated pixel flow map and the warped image are judged by the discriminator. As texture far away from the image center has strong distortion, we develop an Adaptive Inverted Foveal layer which can transform the deformation to the intensity of the image to exploit this property. Rotation symmetry enhanced convolution kernels are applied to extract geometric features of different orientations explicitly. These learned features are recalibrated using the Squeeze-and-Excitation block to assign different weights for different directions. Moreover, we construct a first real-world radial distorted image dataset RD600 annotated with ground truth to evaluate our proposed method. We conduct extensive experiments to validate the effectiveness of each part of our framework. The further experiment shows our approach outperforms previous methods in both synthetic and real-world datasets quantitatively and qualitatively.