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This article introduces the solutions of the team lvisTraveler for LVISChallenge 2020. In this work, two characteristics of LVIS dataset are mainlyconsidered: the long-tailed distribution and high quality instance segmentationmask. We adopt a two-stage training pipeline. In the first stage, weincorporate EQL and self-training to learn generalized representation. In thesecond stage, we utilize Balanced GroupSoftmax to promote the classifier, andpropose a novel proposal assignment strategy and a new balanced mask loss formask head to get more precise mask predictions. Finally, we achieve 41.5 and41.2 AP on LVIS v1.0 val and test-dev splits respectively, outperforming thebaseline based on X101-FPN-MaskRCNN by a large margin.

1st Place Solution of LVIS Challenge 2020: A Good Box is not a Guarantee of a Good Mask