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SOTA
Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation On 11
Unsupervised Video Object Segmentation On 11
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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
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Modellname
J
Paper Title
Repository
TMO (MiT-b1)
80.0
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TMO++ (MiT-b1)
83.2
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
MATNet
76.1
Motion-Attentive Transition for Zero-Shot Video Object Segmentation
GSANet
83.1
Guided Slot Attention for Unsupervised Video Object Segmentation
TransportNet
78.7
Deep Transport Network for Unsupervised Video Object Segmentation
-
IMP
77.5
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
-
TMO++ (RN-101)
81.2
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
PDB
74.0
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
-
AMC-Net
76.5
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
-
COSNet
75.6
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
DPA
83.4
Dual Prototype Attention for Unsupervised Video Object Segmentation
F2Net
77.5
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation
-
FakeFlow
84.7
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
TMO (RN-101)
79.9
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
PMN
77.7
Unsupervised Video Object Segmentation via Prototype Memory Network
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