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 125 of 89 papers

TitleStatusHype
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation0
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
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
Treating Motion as Option with Output Selection for Unsupervised Video Object SegmentationCode1
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation0
Tracking Anything with Decoupled Video SegmentationCode3
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything ModelCode1
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
Guided Slot Attention for Unsupervised Video Object SegmentationCode1
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
Dual Prototype Attention for Unsupervised Video Object SegmentationCode1
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance0
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
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