mlsanityData to 7 Oct 2026

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

arXiv 2009.01559 ↗First posted 3 Sep 2020cs.CVcited by 15

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Abstract (arXiv)

This article introduces the solutions of the team lvisTraveler for LVIS Challenge 2020. In this work, two characteristics of LVIS dataset are mainly considered: the long-tailed distribution and high quality instance segmentation mask. We adopt a two-stage training pipeline. In the first stage, we incorporate EQL and self-training to learn generalized representation. In the second stage, we utilize Balanced GroupSoftmax to promote the classifier, and propose a novel proposal assignment strategy and a new balanced mask loss for mask head to get more precise mask predictions. Finally, we achieve 41.5 and 41.2 AP on LVIS v1.0 val and test-dev splits respectively, outperforming the baseline based on X101-FPN-MaskRCNN by a large margin.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 1; 2024: 1; 2025: 1.

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