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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

Showing 3140 of 89 papers

TitleStatusHype
A Simple and Powerful Global Optimization for Unsupervised Video Object SegmentationCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier0
Instance Embedding Transfer to Unsupervised Video Object Segmentation0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation0
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation0
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation0
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