DAVIS 2016
Emerging9papers using it
2018first seen
'DAVIS 2016' is a benchmark dataset used to evaluate semi-supervised video object segmentation (VOS) performance, containing video sequences with annotated object masks.
Papers using DAVIS 2016 (9)
- Moving Object Detection from Moving Camera Using Focus of Expansion Likelihood and SegmentationFast Online Object Tracking and Segmentation: A Unifying ApproachCollaborative Video Object Segmentation by Foreground-Background
IntegrationUnsupervised Video Object Segmentation with Distractor-Aware Online
AdaptationReConvNet: Video Object Segmentation with Spatio-Temporal Features
ModulationFast Pixel-Matching for Video Object SegmentationA Simple and Powerful Global Optimization for Unsupervised Video Object
SegmentationHierarchical Spatiotemporal Transformers for Video Object SegmentationOn guiding video object segmentation