Semantic Edge Detection with Diverse Deep Supervision

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Overview

Semantic Edge Detection with Diverse Deep Supervision

This repository contains the code for our IJCV paper: "Semantic Edge Detection with Diverse Deep Supervision". The code is based on the Caffe framework which is outdated now (the reviewing process lasts very very long). Hence, we only release the code for the future reference. If you want to do something about the task of semantic edge detection, the code for the DFF paper ("Dynamic Feature Fusion for Semantic Edge Detection", IJCAI 2019) is a good choice to start with, which is based on the popular PyTorch framework. If you really want to run our Caffe code, please prepare data like the SEAL repository ("Simultaneous Edge Alignment and Learning", ECCV 2018), although I guess that no one would like to do so...

Citations

@article{liu2021semantic,
  title={Semantic edge detection with diverse deep supervision},
  author={Liu, Yun and Cheng, Ming-Ming and Fan, Deng-Ping and Zhang, Le and Bian, JiaWang and Tao, Dacheng},
  journal={International Journal of Computer Vision},
  year={2021}
}
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Comments
  • Questions about evaluations

    Questions about evaluations

    After reading your paper, I noticed that you have used Gated-SCNN for your comparison. As far as I know, GSCNN outputs semantic segmentation and category agnostic edge maps. In the paper, you described the process of evaluation as "Gated-SCNN learns semantic edges for improving the training of semantic segmentation. Hence, we retain it for semantic edge detection by removing its segmentation loss and dual task loss, and the other settings are kept by default." I'm curious on how to extract semantic edges in Gated-SCNN since looking at their code and paper, it seems like the edges are binary maps (maybe you could mask the segmentation maps with the edge maps, but it doesn't seem like what you guys did). Could you explain the process?

    opened by haruishi43 2
Owner
Yun Liu
Postdoc, ETH Zurich
Yun Liu
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