Impact of data smoothing on semantic segmentation

Semantic segmentation is the process to classify each pixel of an image. The current state-of-the-art semantic segmentation techniques use end-to-end trainable deep models. Generally, the training of these models is controlled by some external hyper-parameters rather to use the variation in data. In this paper, we investigate the impact of data smoothing on the training and generalization of deep semantic segmentation models. A mechanism is proposed to select the best level of smoothing to get better generalization of the deep semantic segmentation models. Furthermore, a smoothing layer is included in the deep semantic segmentation models to automatically adjust the level of smoothing. Extensive experiments are performed to validate the effectiveness of the proposed smoothing strategies.