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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 1120 of 89 papers

TitleStatusHype
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
Guided Slot Attention for Unsupervised Video Object SegmentationCode1
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation0
Flow-guided Semi-supervised Video Object Segmentation0
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy0
Dual Prototype Attention for Unsupervised Video Object SegmentationCode1
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance0
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