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 2130 of 89 papers

TitleStatusHype
A Simple and Powerful Global Optimization for Unsupervised Video Object SegmentationCode1
Unsupervised Video Object Segmentation via Prototype Memory NetworkCode1
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object SegmentationCode1
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut0
Hierarchical Feature Alignment Network for Unsupervised Video Object SegmentationCode1
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
In-N-Out Generative Learning for Dense Unsupervised Video SegmentationCode1
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier0
Autoencoder-based background reconstruction and foreground segmentation with background noise estimationCode1
Learning To Segment Dominant Object Motion From Watching Videos0
Show:102550
← PrevPage 3 of 9Next →

No leaderboard results yet.