Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge
- URL: http://arxiv.org/abs/2007.08919v1
- Date: Fri, 17 Jul 2020 11:49:10 GMT
- Title: Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge
- Authors: Chih-Chung Hsu and Hsin-Ti Ma
- Abstract summary: Current state-of-the-art and deep learning-based semantic segmentation techniques are hard to train well.
We propose edge-preserving guidance to obtain the extra prior information.
Experiments demonstrate that the proposed method can achieve excellent performance under small-scale training set.
- Score: 3.435043566706133
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic segmentation is one of the most attractive research fields in
computer vision. In the VIPriors challenge, only very limited numbers of
training samples are allowed, leading to that the current state-of-the-art and
deep learning-based semantic segmentation techniques are hard to train well. To
overcome this shortcoming, therefore, we propose edge-preserving guidance to
obtain the extra prior information, to avoid the overfitting under small-scale
training dataset. First, a two-channeled convolutional layer is concatenated to
the last layer of the conventional semantic segmentation network. Then, an edge
map is calculated from the ground truth by Sobel operation and followed by
concatenating a hard-thresholding operation to indicate whether the pixel is
the edge or not. Then, the two-dimensional cross-entropy loss is adopted to
calculate the loss between the predicted edge map and its ground truth, termed
as an edge-preserving loss. In this way, the continuity of boundaries between
different instances can be forced by the proposed edge-preserving loss.
Experiments demonstrate that the proposed method can achieve excellent
performance under small-scale training set, compared to state-of-the-art
semantic segmentation techniques.
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