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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 1–50 of 89 papers

TitleStatusHype
Tracking Anything with Decoupled Video SegmentationCode3
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
Treating Motion as Option with Output Selection for Unsupervised Video Object SegmentationCode1
UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything ModelCode1
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
Guided Slot Attention for Unsupervised Video Object SegmentationCode1
Dual Prototype Attention for Unsupervised Video Object SegmentationCode1
A Simple and Powerful Global Optimization for Unsupervised Video Object SegmentationCode1
Unsupervised Video Object Segmentation via Prototype Memory NetworkCode1
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object SegmentationCode1
Hierarchical Feature Alignment Network for Unsupervised Video Object SegmentationCode1
In-N-Out Generative Learning for Dense Unsupervised Video SegmentationCode1
Autoencoder-based background reconstruction and foreground segmentation with background noise estimationCode1
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
Dense Unsupervised Learning for Video SegmentationCode1
Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object SegmentationCode1
Full-Duplex Strategy for Video Object SegmentationCode1
Reciprocal Transformations for Unsupervised Video Object SegmentationCode1
Video Instance Segmentation with a Propose-Reduce ParadigmCode1
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object SegmentationCode1
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object SegmentationCode1
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in VideosCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
Motion-Attentive Transition for Zero-Shot Video Object SegmentationCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
Zero-Shot Video Object Segmentation via Attentive Graph Neural NetworksCode1
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese NetworksCode1
UnOVOST: Unsupervised Offline Video Object Segmentation and TrackingCode1
Tukey-Inspired Video Object SegmentationCode1
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention—0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation—0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation—0
Flow-guided Semi-supervised Video Object Segmentation—0
Unsupervised Video Object Segmentation with Online Adversarial Self-Tuning—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance—0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut—0
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier—0
Learning To Segment Dominant Object Motion From Watching Videos—0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Mask Selection and Propagation for Unsupervised Video Object SegmentationCode0
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