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

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Showing 26–50 of 89 papers

TitleStatusHype
Implicit Motion-Compensated Network for Unsupervised Video Object SegmentationCode0
In-N-Out Generative Learning for Dense Unsupervised Video SegmentationCode1
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier—0
Autoencoder-based background reconstruction and foreground segmentation with background noise estimationCode1
Learning To Segment Dominant Object Motion From Watching Videos—0
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
Video Salient Object Detection via Contrastive Features and Attention Modules—0
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
Mask Selection and Propagation for Unsupervised Video Object SegmentationCode0
Deep Transport Network for Unsupervised Video Object Segmentation—0
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object SegmentationCode1
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation—0
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object SegmentationCode1
DyStaB: Unsupervised Object Segmentation via Dynamic-Static Bootstrapping—0
Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation—0
Unsupervised Video Object Segmentation with Joint Hotspot Tracking—0
ALBA : Reinforcement Learning for Video Object SegmentationCode0
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in VideosCode1
Learning Video Object Segmentation from Unlabeled VideosCode1
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