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.
Original language | English |
---|---|
Title of host publication | Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
Publisher | IEEE Computer Society |
Pages | 7482-7491 |
Number of pages | 10 |
ISBN (Electronic) | 9781728132938 |
DOIs | |
State | Published - Jun 2019 |
Event | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
---|---|
Volume | 2019-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
---|---|
Country/Territory | United States |
City | Long Beach |
Period | 16/06/19 → 20/06/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Grouping and Shape
- Scene Analysis and Understanding
- Segmentation