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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 5160 of 89 papers

TitleStatusHype
Motion-Attentive Transition for Zero-Shot Video Object SegmentationCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
Zero-Shot Video Object Segmentation via Attentive Graph Neural NetworksCode1
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese NetworksCode1
UnOVOST: Unsupervised Offline Video Object Segmentation and TrackingCode1
Anchor Diffusion for Unsupervised Video Object SegmentationCode0
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion SaliencyCode0
Joint-task Self-supervised Learning for Temporal CorrespondenceCode0
Semi-Supervised Video Salient Object Detection Using Pseudo-LabelsCode0
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation0
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