Edge-aware Plug-and-play Scheme for Semantic Segmentation
Jianye Yi, Xiaopin Zhong, Weixiang Liu, Wenxuan Zhu and, Zongze Wu, Yuanlong Deng

TL;DR
This paper introduces a universal, plug-and-play edge supervision scheme for semantic segmentation that enhances performance across various models without requiring architecture modifications.
Contribution
The proposed Edge-aware Plug-and-play Scheme (EPS) is a universal method that can be applied to any segmentation model to improve edge preservation and segmentation accuracy.
Findings
EPS improves segmentation performance on Cityscapes dataset
Compatible with 22 different models without architecture changes
Uses a novel Polar Hausdorff Loss for boundary preservation
Abstract
Semantic segmentation is a classic and fundamental computer vision problem dedicated to assigning each pixel with its corresponding class. Some recent methods introduce edge-based information for improving the segmentation performance. However these methods are specific and limited to certain network architectures, and they can not be transferred to other models or tasks. Therefore, we propose an abstract and universal edge supervision method called Edge-aware Plug-and-play Scheme (EPS), which can be easily and quickly applied to any semantic segmentation models. The core is edge-width/thickness preserving guided for semantic segmentation. The EPS first extracts the Edge Ground Truth (Edge GT) with a predefined edge thickness from the training data; and then for any network architecture, it directly copies the decoder head for the auxiliary task with the Edge GT supervision. To ensure…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Visual Attention and Saliency Detection
