In this paper, we aim at automatically searching an efficient network architecture for dense image prediction. Particularly, we follow the encoder-decoder style and focus on automatically designing a connectivity structure for the decoder. To achieve that, we first design a densely connected network with learnable connections named Fully Dense Network, which contains a large set of possible final connectivity structures. We then employ gradient descent to search the optimal connectivity from the dense connections. The search process is guided by a novel loss function, which pushes the weight of each connection to be binary and the connections to be sparse. The discovered connectivity achieves competitive results on two segmentation datasets, while runs more than three times faster and requires less than half parameters compared to state-of-the-art methods. An extensive experiment shows that the discovered connectivity is compatible with various backbones and generalizes well to other dense image prediction tasks.

本文提出了一种名为Fully Dense Network的密集连接网络结构，通过输入较少的参数，自动搜索适用于密集图像预测的高效网络体系结构，该体系结构具有较高的性能和竞争力，并适用于多种不同的图像预测任务。

SparseMask: 密集图像预测的可微连通性学习