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Papers/Guided Interactive Video Object Segmentation Using Reliabi...

Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Yuk Heo, Yeong Jun Koh, Chang-Su Kim

2021-04-21CVPR 2021 1Interactive SegmentationSegmentationSemantic SegmentationVideo Object SegmentationInteractive Video Object SegmentationVideo Semantic Segmentation
PaperPDFCode(official)

Abstract

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.

Results

TaskDatasetMetricValueModel
VideoDAVIS 2017AUC-J0.82GIS
VideoDAVIS 2017AUC-J&F0.856GIS
VideoDAVIS 2017J&F@60s0.866GIS
VideoDAVIS 2017J@60s0.829GIS
Video Object SegmentationDAVIS 2017AUC-J0.82GIS
Video Object SegmentationDAVIS 2017AUC-J&F0.856GIS
Video Object SegmentationDAVIS 2017J&F@60s0.866GIS
Video Object SegmentationDAVIS 2017J@60s0.829GIS

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