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We describe our two-stage instance segmentation framework we use to competein the challenge. The first stage of our framework consists of an objectdetector, which generates object proposals in the format of bounding boxes.Then, the images and the detected bounding boxes are fed to the second stage,where a segmentation network is applied to segment the objects in the boundingboxes. We train all our networks in a class-agnostic way. Our approach achievesthe first place in the UVO 2021 Image-based Open-World Segmentation Challenge.

1st Place Solution for the UVO Challenge on Image-based Open-World Segmentation 2021