FCIS

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Implementations

Introduction

FCIS is a fully convolutional end-to-end solution for instance segmentation, which won the first place in COCO segmentation challenge 2016.

FCIS is initially described in a CVPR 2017 spotlight paper. It is worth noticing that:

  • FCIS provides a simple, fast and accurate framework for instance segmentation.
  • Different from MNC, FCIS performs instance mask estimation and categorization jointly and simultanously, and estimates class-specific masks.
  • We did not exploit the various techniques & tricks in the Mask RCNN system, like increasing RPN anchor numbers (from 12 to 15), training on anchors out of image boundary, enlarging the image (shorter side from 600 to 800 pixels), utilizing FPN features and aligned ROI pooling. These techniques & tricks should be orthogonal to our simple baseline

Github: https://github.com/msracver/FCIS

instance-segmentationfcis
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