论文标题
具有分裂标准化的神经网络，用于图像分割，并在城市景观数据集中应用
Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset
论文作者
论文摘要
计算机视觉中的关键问题之一是适应：模型太僵化了，无法遵循输入的可变性。解释感觉神经科学适应的规范计算是分裂的归一化，并且对图像歧管具有吸引力的影响。在这项工作中，我们表明，在当前的深网中包括分裂的归一化，使它们对图像的非信息变化更加不变。特别是，我们专注于图像分割的U-NET体系结构。实验表明，在U-NET体系结构中包含分裂的归一化会导致相对于常规U-NET提供更好的分割结果。在处理恶劣天气条件下获得的图像时，收益会稳定增加。除了在城市景观和雾gy的城市景观数据集上的结果外，我们还通过可视化响应来解释这些优势：分裂归一化引起的均等化导致对比和照明的局部变化更不变的特征。
One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we focus on U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better segmentation results with respect to conventional U-Net. The gain increases steadily when dealing with images acquired in bad weather conditions. In addition to the results on the Cityscapes and Foggy Cityscapes datasets, we explain these advantages through visualization of the responses: the equalization induced by the divisive normalization leads to more invariant features to local changes in contrast and illumination.
