SOTAVerified

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 7180 of 89 papers

TitleStatusHype
Joint-task Self-supervised Learning for Temporal CorrespondenceCode0
Semi-Supervised Video Salient Object Detection Using Pseudo-LabelsCode0
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
RVOS: End-to-End Recurrent Network for Video Object SegmentationCode0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion SaliencyCode0
Learning Unsupervised Video Object Segmentation Through Visual AttentionCode0
Video Object Segmentation using Supervoxel-Based GerrymanderingCode0
Anchor Diffusion for Unsupervised Video Object SegmentationCode0
Unsupervised Moving Object Detection via Contextual Information SeparationCode0
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