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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 2130 of 89 papers

TitleStatusHype
Video Instance Segmentation with a Propose-Reduce ParadigmCode1
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object SegmentationCode1
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object SegmentationCode1
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in VideosCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
Motion-Attentive Transition for Zero-Shot Video Object SegmentationCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese NetworksCode1
Zero-Shot Video Object Segmentation via Attentive Graph Neural NetworksCode1
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