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Unsupervised Video Object Segmentation On 10

Metrics

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Results

Performance results of various models on this benchmark

Paper Title
DEVA (DIS)90.288.987.6Tracking Anything with Decoupled Video Segmentation
GSANet89.688.988.3Guided Slot Attention for Unsupervised Video Object Segmentation
FakeFlow89.088.588.0Improving Unsupervised Video Object Segmentation via Fake Flow Generation
DPA88.487.686.8Dual Prototype Attention for Unsupervised Video Object Segmentation
TMO (MiT-b1)87.887.286.6Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
AMP87.587.387.1Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
IMP86.785.684.5Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
DLDA86.685.7584.9Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention
TMO (RN-101)86.686.185.6Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
D2Conv3D86.586.085.5D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos
PMN86.485.985.4Unsupervised Video Object Segmentation via Prototype Memory Network
TransportNet85.084.884.5Deep Transport Network for Unsupervised Video Object Segmentation
RTNet84.785.285.6Reciprocal Transformations for Unsupervised Video Object Segmentation
3DC-Seg84.784.584.3Making a Case for 3D Convolutions for Object Segmentation in Videos
AMC-Net84.684.684.5Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
FSNet83.183.383.4Full-Duplex Strategy for Video Object Segmentation
DFNet81.882.683.4Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation
MATNet80.781.682.4Motion-Attentive Transition for Zero-Shot Video Object Segmentation
WCS-Net80.781.582.2Unsupervised Video Object Segmentation with Joint Hotspot Tracking
AD-Net80.581.181.7Anchor Diffusion for Unsupervised Video Object Segmentation
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