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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

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Showing 51–89 of 89 papers

TitleStatusHype
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Instance Embedding Transfer to Unsupervised Video Object Segmentation—0
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier—0
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos—0
Learning Motion Patterns in Videos—0
Learning To Segment Dominant Object Motion From Watching Videos—0
Learning to Segment Moving Objects—0
Learning Video Object Segmentation with Visual Memory—0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation using Motion Saliency-Guided Spatio-Temporal Propagation—0
Motion Trajectory Segmentation via Minimum Cost Multicuts—0
Primary Object Segmentation in Videos Based on Region Augmentation and Reduction—0
Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions—0
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection—0
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation—0
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation with Motion-based Bilateral Networks—0
Learning Correspondence from the Cycle-Consistency of TimeCode0
Joint-task Self-supervised Learning for Temporal CorrespondenceCode0
Semi-Supervised Video Salient Object Detection Using Pseudo-LabelsCode0
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
RVOS: End-to-End Recurrent Network for Video Object SegmentationCode0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
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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