mlsanityData to 14 Oct 2026

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

arXiv 2110.10239 ↗First posted 19 Oct 2021cs.CVcited by 8

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Abstract (arXiv)

We describe our two-stage instance segmentation framework we use to compete in the challenge. The first stage of our framework consists of an object detector, 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 bounding boxes. We train all our networks in a class-agnostic way. Our approach achieves the first place in the UVO 2021 Image-based Open-World Segmentation Challenge.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

20 of 26 references.

References · 20

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8 more
  1. 2018AutoAugment: Learning Augmentation Policies from Datacited by 730
  2. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 596
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 3.

8 papers.

  1. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  2. 2023CNOS: A Strong Baseline for CAD-based Novel Object Segmentationcited by 87
  3. 2023DiffTAD: Temporal Action Detection with Proposal Denoising Diffusioncited by 19
  4. 2022PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Trackingcited by 15
  5. 2023Table Detection for Visually Rich Document Imagescited by 6
  6. 2022Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularizationcited by 4
  7. 2021UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solutioncited by 3
  8. 20221st Place Solutions for the UVO Challenge 2022cited by 1