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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 3140 of 89 papers

TitleStatusHype
A Simple and Powerful Global Optimization for Unsupervised Video Object SegmentationCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
Self-supervised Learning for Video Correspondence FlowCode0
Semi-Supervised Video Salient Object Detection Using Pseudo-LabelsCode0
SegFlow: Joint Learning for Video Object Segmentation and Optical FlowCode0
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
Anchor Diffusion for Unsupervised Video Object SegmentationCode0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
Extending Layered Models to 3D MotionCode0
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