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Found 4 papers, 4 papers with code
DVIS++: Improved Decoupled Framework for Universal Video Segmentation
We present the \textbf{D}ecoupled \textbf{VI}deo \textbf{S}egmentation (DVIS) framework, a novel approach for the challenging task of universal video segmentation, including video instance segmentation (VIS), video semantic segmentation (VSS), and video panoptic segmentation (VPS).
Ranked #1 on
Video Semantic Segmentation
on VSPW
1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation
Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving.
1st Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation
In this report, we successfully validated the effectiveness of the decoupling strategy in video panoptic segmentation.
DVIS: Decoupled Video Instance Segmentation Framework
The efficacy of the decoupling strategy relies on two crucial elements: 1) attaining precise long-term alignment outcomes via frame-by-frame association during tracking, and 2) the effective utilization of temporal information predicated on the aforementioned accurate alignment outcomes during refinement.
Ranked #4 on
Video Panoptic Segmentation
on VIPSeg
