Title: Rethinking Knowledge Distillation with Raw Features for Semantic Segmentation. Abstract: Most existing knowledge distillation methods for semantic segmentation focus on extracting various complex forms of knowledge from raw features. However, such knowledge is usually manually designed and relies on prior knowledge as in traditional feature engineering. In this paper, in order to seek a more simple and effective way to perform feature distillation, we analyze the naive feature distillation method with raw features and reveal that it actually attempts to make the student learn both the magnitude and angular information from the teacher features simultaneously. We further find experimentally that the angular information is more effective than the magnitude information for feature distillation. Based on this finding, we propose a simple and effective feature distillation method for semantic segmentation, which eliminates the need to manually design distillation knowledge. Experimental results on three popular benchmark datasets show that our method achieves state-of-the-art distillation performance for semantic segmentation. The code will be available.