Semantic Segmentation via Domain Adaptation with Global Structure Embedding In this paper we focus on the problem of unsupervised domain adaptation for semantic segmentation. The previous works usually focus on adversarial learning either in pixellevel or featurelevel. However, global structure knowledge is often neglected in the adversarial learning due to the possible reasons: First, the result of pixellevel adversarial learning does not necessarily preserve the semantic consistency of the input image. Second, global structure knowledge is not embedded to regularize the featurelevel adversarial learning. In this work, we propose a framework for unsupervised domain adaptation in semantic segmentation which effectively incorporates pixel level, featurelevel adversarial learning and selftraining strategy. Our framework embeds the global structure knowledge into the adversarial training step to tackle the problem of structure misalignment. Consequently, our proposed framework achieves the stateoftheart semantic segmentation domain adaptation results on the task of transferring GTA5 to Cityscapes.

Taskinall Domain Adaptation for Semantic Segmentation In this work we tackle the problem of unsupervised domain adaptation for semantic segmentation. One pipeline is to sequentially train imagetranslation model and the final task segmentation model. In such pipeline, image translation is aimed to generate the translated sourcedomain images which are visually similar to the targetdomain images and then the final task model is trained using the translated images and its corresponding groundtruth. However, the visually optimal translatedimages are not necessarily optimal for the final task of segmenting the targetdomain images. Thus we propose a Taskinall pipeline for unsupervised domain adaptation on semantic segmentation, which incorporates image translation and final segmentation task into an endtoend training pipeline. Our aim is to generate the translated images which better assists the final task, instead of just being visually similar to the target domain images. We show that in the task of adapting from GTA5 to Cityscapes dataset, the segmentation performance of our Taskinall pipeline outperforms the sequentially training pipeline, with simpler model structure and less training complexity.