Video Instance Segmentation with a Propose-Reduce Paradigm
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete results by tracking or matching. These methods may cause error accumulation in the merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step. We further build a sequence propagation head on the existing image-level instance segmentation network for long-term propagation. To ensure robustness and high recall of our proposed framework, multiple sequences are proposed where redundant sequences of the same instance are reduced. We achieve state-of-the-art performance on two representative benchmark datasets -- we obtain 47.6% in terms of AP on YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code is available at https://github.com/dvlab-research/ProposeReduce.
PDF Abstract ICCV 2021 PDF ICCV 2021 Abstract
MS COCO
DAVIS
DAVIS 2017
YouTube-VIS 2019
Referring Expressions for DAVIS 2016 & 2017
Results from the Paper
Ranked #2 on
Unsupervised Video Object Segmentation
on DAVIS 2017 (val)
(using extra training data)
