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5 months ago

Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

Heo Yuk ; Koh Yeong Jun ; Kim Chang-Su

Guided Interactive Video Object Segmentation Using Reliability-Based
  Attention Maps

Abstract

We propose a novel guided interactive segmentation (GIS) algorithm for videoobjects to improve the segmentation accuracy and reduce the interaction time.First, we design the reliability-based attention module to analyze thereliability of multiple annotated frames. Second, we develop theintersection-aware propagation module to propagate segmentation results toneighboring frames. Third, we introduce the GIS mechanism for a user to selectunsatisfactory frames quickly with less effort. Experimental resultsdemonstrate that the proposed algorithm provides more accurate segmentationresults at a faster speed than conventional algorithms. Codes are available athttps://github.com/yuk6heo/GIS-RAmap.

Code Repositories

yuk6heo/GIS-RAmap
Official
pytorch

Benchmarks

BenchmarkMethodologyMetrics
interactive-video-object-segmentation-onGIS
AUC-J: 0.820
AUC-Ju0026F: 0.856
Ju0026F@60s: 0.866
J@60s: 0.829

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Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps | Papers | HyperAI