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insta_yolo.md

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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