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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 7689 of 89 papers

TitleStatusHype
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion SaliencyCode0
Learning Unsupervised Video Object Segmentation Through Visual AttentionCode0
Video Object Segmentation using Supervoxel-Based GerrymanderingCode0
Anchor Diffusion for Unsupervised Video Object SegmentationCode0
Unsupervised Moving Object Detection via Contextual Information SeparationCode0
Mask Selection and Propagation for Unsupervised Video Object SegmentationCode0
Unsupervised Online Video Object Segmentation with Motion Property UnderstandingCode0
Unsupervised Video Object Segmentation for Deep Reinforcement LearningCode0
SegFlow: Joint Learning for Video Object Segmentation and Optical FlowCode0
A 3D Convolutional Approach to Spectral Object Segmentation in Space and TimeCode0
Self-supervised Learning for Video Correspondence FlowCode0
Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) SettingCode0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
Extending Layered Models to 3D MotionCode0
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