Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps
We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
PDF Abstract CVPR 2021 PDF CVPR 2021 AbstractCode
Datasets
DAVIS
DAVIS 2017
YouTube-VOS 2018
Results from the Paper
Ranked #2 on
Interactive Video Object Segmentation
on DAVIS 2017
(using extra training data)
