  Semantic segmentation is a fundamental task in computer vision, which can be
considered as a per-pixel classification problem. Recently, although fully
convolutional neural network (FCN) based approaches have made remarkable
progress in such task, aggregating local and contextual information in
convolutional feature maps is still a challenging problem. In this paper, we
argue that, when predicting the category of a given pixel, the regions close to
the target are more important than those far from it. To tackle this problem,
we then propose an effective yet efficient approach named Vortex Pooling to
effectively utilize contextual information. Empirical studies are also provided
to validate the effectiveness of the proposed method. To be specific, our
approach outperforms the previous state-of-the-art model named DeepLab v3 by
1.5% on the PASCAL VOC 2012 val set and 0.6% on the test set by replacing the
Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed
Vortex Pooling. Moreover, our model (10.13FPS) shares similar computation cost
with DeepLab v3 (10.37 FPS).
