Hierarchically Gated Deep Networks for Semantic Segmentation
Semantic segmentation aims to parse the scene structure of images by annotating the labels to each pixel so that images can be segmented into different regions. While image structures usually have various scales, it is difficult to use a single scale to model the spatial contexts for all individual pixels. Multi scale Convolutional Neural Networks (CNNs) and their variants have made striking success for modeling the global scene structure for an image. However, they are limited in labeling fine grained local structures like pixels and patches, since spatial contexts might be blindly mixed up without appropriately customizing their scales. To address this challenge, we develop a novel paradigm of multiscale deep network to model spatial contexts surrounding different pixels at various scales. It builds multiple layers of memory cells, learning feature representations for individual pixels at their customized scales by hierarchically absorbing relevant spatial contexts via memory gates between layers. Such Hierarchically Gated Deep Networks (HGDNs) can customize a suitable scale for each pixel, thereby delivering better performance on labeling scene structures of various scales. We conduct the experiments on two datasets, and show competitive results compared with the other multi scale deep networks on the semantic segmentation task.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)