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Unsupervised Video Object Segmentation On 11

Metrics

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Results

Performance results of various models on this benchmark

Paper Title
FakeFlow84.7Improving Unsupervised Video Object Segmentation via Fake Flow Generation
DPA83.4Dual Prototype Attention for Unsupervised Video Object Segmentation
TMO++ (MiT-b1)83.2Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
GSANet83.1Guided Slot Attention for Unsupervised Video Object Segmentation
TMO++ (RN-101)81.2Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
TMO (MiT-b1)80.0Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TMO (RN-101)79.9Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TransportNet78.7Deep Transport Network for Unsupervised Video Object Segmentation
PMN77.7Unsupervised Video Object Segmentation via Prototype Memory Network
IMP77.5Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
F2Net77.5F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
AMC-Net76.5Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
MATNet76.1Motion-Attentive Transition for Zero-Shot Video Object Segmentation
COSNet75.6See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
PDB74.0Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
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