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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 76–89 of 89 papers

TitleStatusHype
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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