Edge detection has attracted considerable attention thanks to its exceptional
ability to enhance performance in downstream computer vision tasks. In recent
years, various deep learning methods have been explored for edge detection
tasks resulting in a significant performance improvement compared to
conventional computer vision algorithms. In neural networks, edge detection
tasks require considerably large receptive fields to provide satisfactory
performance. In a typical convolutional operation, such a large receptive field
can be achieved by utilizing a significant number of consecutive layers, which
yields deep network structures. Recently, a Multi-scale Tensorial Summation
(MTS) factorization operator was presented, which can achieve very large
receptive fields even from the initial layers. In this paper, we propose a
novel MTS Dimensional Reduction (MTS-DR) module guided neural network,
MTS-DR-Net, for the edge detection task. The MTS-DR-Net uses MTS layers, Und
corresponding MTS-DR blocks as a new backbone to remove redundant information
initially. Such a dimensional reduction module enables the neural network to
focus specifically on relevant information (i.e., necessary subspaces).
Finally, a weight U-shaped refinement module follows MTS-DR blocks in the
MTS-DR-Net. We conducted extensive experiments on two benchmark edge detection
datasets: BSDS500 and BIPEDv2 to verify the effectiveness of our model. The
implementation of the proposed MTS-DR-Net can be found at
https://github.com/LeiXuAI/MTS-DR-Net.git.

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