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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 110 of 89 papers

TitleStatusHype
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation0
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
Improving Unsupervised Video Object Segmentation via Fake Flow Generation0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation0
Treating Motion as Option with Output Selection for Unsupervised Video Object SegmentationCode1
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation0
Tracking Anything with Decoupled Video SegmentationCode3
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything ModelCode1
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