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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 125 of 89 papers

TitleStatusHype
Tracking Anything with Decoupled Video SegmentationCode3
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese NetworksCode1
A Simple and Powerful Global Optimization for Unsupervised Video Object SegmentationCode1
Multi-Source Fusion and Automatic Predictor Selection for Zero-Shot Video Object SegmentationCode1
Dense Unsupervised Learning for Video SegmentationCode1
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in VideosCode1
Learning Motion and Temporal Cues for Unsupervised Video Object SegmentationCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
MAST: A Memory-Augmented Self-supervised TrackerCode1
Motion-Attentive Transition for Zero-Shot Video Object SegmentationCode1
Guided Slot Attention for Unsupervised Video Object SegmentationCode1
Autoencoder-based background reconstruction and foreground segmentation with background noise estimationCode1
Dual Prototype Attention for Unsupervised Video Object SegmentationCode1
Bootstrapping Objectness from Videos by Relaxed Common Fate and Visual GroupingCode1
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
In-N-Out Generative Learning for Dense Unsupervised Video SegmentationCode1
D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object SegmentationCode1
D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in VideosCode1
Full-Duplex Strategy for Video Object SegmentationCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object SegmentationCode1
Reciprocal Transformations for Unsupervised Video Object SegmentationCode1
Hierarchical Feature Alignment Network for Unsupervised Video Object SegmentationCode1
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object SegmentationCode1
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