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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 71–80 of 89 papers

TitleStatusHype
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos—0
Learning Motion Patterns in Videos—0
Learning To Segment Dominant Object Motion From Watching Videos—0
Learning to Segment Moving Objects—0
Learning Video Object Segmentation with Visual Memory—0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation—0
Motion Trajectory Segmentation via Minimum Cost Multicuts—0
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