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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
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object SegmentationCode1
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation0
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object SegmentationCode1
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping0
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in VideosCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
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