Unsupervised Video Object Segmentation On 10

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평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	F	G	J	Paper Title	Repository
RTNet	84.7	85.2	85.6	Reciprocal Transformations for Unsupervised Video Object Segmentation	-
FakeFlow	89.0	88.5	88.0	Improving Unsupervised Video Object Segmentation via Fake Flow Generation	-
IMP	86.7	85.6	84.5	Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier	-
TransportNet	85.0	84.8	84.5	Deep Transport Network for Unsupervised Video Object Segmentation	-
TMO (MiT-b1)	87.8	87.2	86.6	Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
AGS	77.4	78.6	79.7	Learning Unsupervised Video Object Segmentation Through Visual Attention	-
PDB	74.5	75.9	77.2	Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection	-
3DC-Seg	84.7	84.5	84.3	Making a Case for 3D Convolutions for Object Segmentation in Videos
GSANet	89.6	88.9	88.3	Guided Slot Attention for Unsupervised Video Object Segmentation
DLDA	86.6	85.75	84.9	Self-supervised Video Object Segmentation with Distillation Learning of Deformable Attention	-
AGNN	79.1	79.9	80.7	Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
PMN	86.4	85.9	85.4	Unsupervised Video Object Segmentation via Prototype Memory Network
TMO (RN-101)	86.6	86.1	85.6	Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
AMP	87.5	87.3	87.1	Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
DEVA (DIS)	90.2	88.9	87.6	Tracking Anything with Decoupled Video Segmentation
MATNet	80.7	81.6	82.4	Motion-Attentive Transition for Zero-Shot Video Object Segmentation
AMC-Net	84.6	84.6	84.5	Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation	-
D2Conv3D	86.5	86.0	85.5	D2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos	-
DFNet	81.8	82.6	83.4	Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation	-
FSNet	83.1	83.3	83.4	Full-Duplex Strategy for Video Object Segmentation

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