mlsanityData to 14 Oct 2026

UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solution

arXiv 2110.11661 ↗First posted 22 Oct 2021cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

In this report, we introduce our (pretty straightforard) two-step "detect-then-match" video instance segmentation method. The first step performs instance segmentation for each frame to get a large number of instance mask proposals. The second step is to do inter-frame instance mask matching with the help of optical flow. We demonstrate that with high quality mask proposals, a simple matching mechanism is good enough for tracking. Our approach achieves the first place in the UVO 2021 Video-based Open-World Segmentation Challenge.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 17 of its 22 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

17 of 22 references.

References · 17

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  12. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
5 more
  1. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  2. 2021Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentationcited by 133
  3. 2019CARAFE: Content-Aware ReAssembly of FEaturescited by 119
  4. 2019Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolutioncited by 57
  5. 20211st Place Solution for the UVO Challenge on Image-based Open-World Segmentation 2021cited by 8

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.

  1. 2023Tracking Anything with Decoupled Video Segmentationcited by 197
  2. 2022Robust Online Video Instance Segmentation with Track Queriescited by 8
  3. 2024Segment Anything for Videos: A Systematic Surveycited by 5