Edgeguided Hierarchically Nested Network for Realtime Semantic Segmentation Semantic segmentation requires both large region context information and rich spatial information. The former can be achieved by a deepenough network structure. However, most spatial information lost in repeated downsample operation in deep Convolutional Neural Networks cannot be easily recovered. In this paper, we propose a novel edgeguided hierarchically nested network to improve both speed and accuracy of realtime semantic segmentation. We introduce Edge Guidance Unit which aims to guide the decoder network to exploit highresolution clues, recover and refine spatial information with low additional computational cost. We also introduce a welldesigned Fusion Block to combine different resolution features along with edge guidance. In experiments we demonstrate that our network stabilizes the small object region while achieving a result of 72.7% mean IoU at 40 FPS on CityScapes test dataset.

On Boosting Semantic Street Scene Segmentation with Weak Supervision Training convolutional networks for semantic segmentation requires perpixel ground truth labels, which are very time consuming and hence costly to obtain. Therefore, in this work, we research and develop a hierarchical deep network architecture and the corresponding loss for semantic segmentation that can be trained from weak supervision, such as bounding boxes or image level labels, as well as from strong perpixel supervision. We demonstrate that the hierarchical structure and the simultaneous training on strong (perpixel) and weak (bounding boxes) labels, even from separate datasets, constantly increases the performance against perpixel only training. Moreover, we explore the more challenging case of adding weak imagelevel labels. We collect street scene images and weak labels from the immense Open Images dataset to generate the OpenScapes dataset, and we use this novel dataset to increase segmentation performance on two established perpixel labeled datasets, Cityscapes and Vistas. We report performance gains up to 13.2% mIoU on crucial street scene classes, and inference speed of 20 fps on a Titan V GPU for Cityscapes at 512 x 1024 resolution. Our network and OpenScapes dataset are shared with the research community.