How Shift Equivariance Impacts Metric Learning for Instance Segmentation

Understand

Metric learning has received conflicting assessments concerning its suitability for solving instance segmentation tasks.

• It has been dismissed as theoretically flawed due to the shift equivariance of the employed CNNs and their respective inability to distinguish same-looking objects.
• Yet it has been shown to yield state of the art results for a variety of tasks, and practical issues have mainly been reported in the context of tile-and-stitch approaches, where discontinuities at tile boundaries have been observed.
• To date, neither of the reported issues have undergone thorough formal analysis.