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 illustrate this concept in 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 the conventional U-Net. The gain increases steadily when dealing with images acquired in bad weather conditions (from 3% of IoU increase in regular weather up to 20% on high fog). In addition to the positive results on the Cityscapes and Foggy Cityscapes datasets, we explain these advantages through the visualization of the responses: the equalization induced by the divisive normalization leads to more invariant features to local changes in contrast and illumination.
Published at: https://www.sciencedirect.com/science/article/pii/S0167865523002209
