Neural Architecture Search (NAS) has shown great potentials in automatically
designing neural network architectures for real-time semantic segmentation.
Unlike previous works that utilize a simplified search space with cell-sharing
way, we introduce a new search space where a lightweight model can be more
effectively searched by replacing the cell-sharing manner with cell-independent
one. Based on this, the communication of local to global information is
achieved through two well-designed modules. For local information exchange, a
graph convolutional network (GCN) guided module is seamlessly integrated as a
communication deliver between cells. For global information aggregation, we
propose a novel dense-connected fusion module (cell) which aggregates
long-range multi-level features in the network automatically. In addition, a
latency-oriented constraint is endowed into the search process to balance the
accuracy and latency. We name the proposed framework as Local-to-Global
Information Communication Network Search (LGCNet). Extensive experiments on
Cityscapes and CamVid datasets demonstrate that LGCNet achieves the new
state-of-the-art trade-off between accuracy and speed. In particular, on
Cityscapes dataset, LGCNet achieves the new best performance of 74.0\% mIoU
with the speed of 115.2 FPS on Titan Xp.

本文提出了一种名为 LGCNet 的神经架构搜索框架，该框架采用新的搜索空间来有效地搜索轻量级模型，并通过引入 GCN 引导模块实现局部信息交换和提出新型的密集连接融合模块来实现全局信息聚合，同时还引入了基于时延的约束来平衡准确性和速度。大量实验证明，LGCNet 在 Cityscapes 和 CamVid 数据集上取得了最新的最先进的准确性和速度的平衡。