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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

Showing 4150 of 89 papers

TitleStatusHype
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
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