SOTAVerified

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

Showing 5160 of 89 papers

TitleStatusHype
Mask Selection and Propagation for Unsupervised Video Object SegmentationCode0
Joint-task Self-supervised Learning for Temporal CorrespondenceCode0
Key Instance Selection for Unsupervised Video Object Segmentation0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation0
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping0
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance0
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation0
Flow-guided Semi-supervised Video Object Segmentation0
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