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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 51–89 of 89 papers

TitleStatusHype
Mask Selection and Propagation for Unsupervised Video Object SegmentationCode0
Joint-task Self-supervised Learning for Temporal CorrespondenceCode0
Key Instance Selection for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking—0
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation—0
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping—0
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation—0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance—0
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation—0
Flow-guided Semi-supervised Video Object Segmentation—0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut—0
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos—0
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation—0
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Instance Embedding Transfer to Unsupervised Video Object Segmentation—0
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier—0
Deep Transport Network for Unsupervised Video Object Segmentation—0
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
Primary Object Segmentation in Videos Based on Region Augmentation and Reduction—0
Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions—0
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection—0
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation—0
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation with Motion-based Bilateral Networks—0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention—0
Causal Video Object Segmentation From Persistence of Occlusions—0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
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