UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solution
This addresses video-based open-world segmentation for computer vision applications, but it is incremental as it builds on existing detection and matching techniques.
The authors tackled video instance segmentation by proposing a two-step detect-then-match method, which achieved first place in the UVO 2021 challenge.
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.