Unsupervised Video Object Segmentation On 11

Results

Performance results of various models on this benchmark

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

0 of 15 row(s) selected.