SOTAVerified

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
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
ALBA : Reinforcement Learning for Video Object SegmentationCode0
Learning Correspondence from the Cycle-Consistency of TimeCode0
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) SettingCode0
Semi-Supervised Video Salient Object Detection Using Pseudo-LabelsCode0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
Unsupervised Moving Object Detection via Contextual Information SeparationCode0
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