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 2650 of 89 papers

TitleStatusHype
Learning Video Object Segmentation from Unlabeled VideosCode1
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
Tukey-Inspired Video Object SegmentationCode1
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation0
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
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation0
Flow-guided Semi-supervised Video Object Segmentation0
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut0
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier0
Learning To Segment Dominant Object Motion From Watching Videos0
Video Salient Object Detection via Contrastive Features and Attention Modules0
Mask Selection and Propagation for Unsupervised Video Object SegmentationCode0
Show:102550
← PrevPage 2 of 4Next →

No leaderboard results yet.