• Corpus ID: 235358676

DISCO: accurate Discrete Scale Convolutions

@article{Sosnovik2021DISCOAD,
  title={DISCO: accurate Discrete Scale Convolutions},
  author={Ivan Sosnovik and Artem Moskalev and Arnold W. M. Smeulders},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.02733}
}
Scale is often seen as a given, disturbing factor in many vision tasks. When doing so it is one of the factors why we need more data during learning. In recent work scale equivariance was added to convolutional neural networks. It was shown to be effective for a range of tasks. We aim for accurate scale-equivariant convolutional neural networks (SE-CNNs) applicable for problems where high granularity of scale and small filter sizes are required. Current SECNNs rely on weight sharing and filter… 
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