insta_yolo.md

INSTA-YOLO: Real-Time Instance Segmentation

March 2021

tl;dr: Extend yolo to perform single-stage instance segmentation.

Overall impression

Insta-yolo adopts a fixed length contour representation, and uses a

Work by Valeo Egypt. Speed is very fast but quality is subpar. Looks like a run-of-the-mill paper.

Key ideas

• Represent masks by a fixed number of contour points (polygons) in Cartesian, and predict the polygons of each object instance through the center of the object.
• GT generation with a deterministic algorithm (dominant points detection).
• Loss
  • Regression loss wrt the GT generated with deterministic algo
  • IoU Loss to compensate for the fact that no unique representation for the object mask using fixed number of vertices.
• This can also work for orientated bbox prediction.

Technical details

• Log Cosh loss: a differentiable alternative to Huber loss (smooth L1 loss).

Notes

• On the detection of dominant points on digital curve