ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation

Di Lin, Dingguo Shen, Siting Shen, Yuanfeng Ji, Dani Lischinski, Daniel Cohen-Or, Hui Huang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7490-7499

Abstract


Multi-scale context information has proven to be essential for object segmentation tasks. Recent works construct the multi-scale context by aggregating convolutional feature maps extracted by different levels of a deep neural network. This is typically done by propagating and fusing features in a one-directional, top-down and bottom-up, manner. In this work, we introduce ZigZagNet, which aggregates a richer multi-context feature map by using not only dense top-down and bottom-up propagation, but also by introducing pathways crossing between different levels of the top-down and the bottom-up hierarchies, in a zig-zag fashion. Furthermore, the context information is exchanged and aggregated over multiple stages, where the fused feature maps from one stage are fed into the next one, yielding a more comprehensive context for improved segmentation performance. Our extensive evaluation on the public benchmarks demonstrates that ZigZagNet surpasses the state-of-the-art accuracy for both semantic segmentation and instance segmentation tasks.

Related Material


[pdf]
[bibtex]
@InProceedings{Lin_2019_CVPR,
author = {Lin, Di and Shen, Dingguo and Shen, Siting and Ji, Yuanfeng and Lischinski, Dani and Cohen-Or, Daniel and Huang, Hui},
title = {ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}