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Found 3 papers, 2 papers with code
How Shift Equivariance Impacts Metric Learning for Instance Segmentation
In our work, we contribute a comprehensive formal analysis of the shift equivariance properties of encoder-decoder-style CNNs, which yields a clear picture of what can and cannot be achieved with metric learning in the face of same-looking objects.
Probabilistic Deep Learning for Instance Segmentation
Probabilistic convolutional neural networks, which predict distributions of predictions instead of point estimates, led to recent advances in many areas of computer vision, from image reconstruction to semantic segmentation.
PatchPerPix for Instance Segmentation
We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with crossovers.
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