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Unsupervised Video Object Segmentation

The unsupervised scenario assumes that the user does not interact with the algorithm to obtain the segmentation masks. Methods should provide a set of object candidates with no overlapping pixels that span through the whole video sequence. This set of objects should contain at least the objects that capture human attention when watching the whole video sequence i.e objects that are more likely to be followed by human gaze.

Papers

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Showing 26–50 of 89 papers

TitleStatusHype
Treating Motion as Option with Output Selection for Unsupervised Video Object SegmentationCode1
Tukey-Inspired Video Object SegmentationCode1
UnOVOST: Unsupervised Offline Video Object Segmentation and TrackingCode1
Unsupervised Video Object Segmentation via Prototype Memory NetworkCode1
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything ModelCode1
Video Instance Segmentation with a Propose-Reduce ParadigmCode1
Zero-Shot Video Object Segmentation via Attentive Graph Neural NetworksCode1
Key Instance Selection for Unsupervised Video Object Segmentation—0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention—0
Causal Video Object Segmentation From Persistence of Occlusions—0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
Deep Transport Network for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping—0
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation—0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance—0
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation—0
Flow-guided Semi-supervised Video Object Segmentation—0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut—0
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos—0
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation—0
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
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