Recurrent Iterative Gating Networks for Semantic Segmentation
This addresses efficiency and performance in semantic segmentation for computer vision applications, though it appears incremental as it builds on existing network architectures.
The paper tackles the problem of improving semantic segmentation by introducing Recurrent Iterative Gating Networks (RIGNet), which use recurrent connections for top-down information flow, resulting in more shallow networks outperforming deeper ones without these modules.
In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information in neural networks in a top-down manner, and different variants on the core structure are considered. The iterative nature of this mechanism allows for gating to spread in both spatial extent and feature space. This is revealed to be a powerful mechanism with broad compatibility with common existing networks. Analysis shows how gating interacts with different network characteristics, and we also show that more shallow networks with gating may be made to perform better than much deeper networks that do not include RIGNet modules.