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SOTA
非監督動画オブジェクトセグメンテーション
Unsupervised Video Object Segmentation On 12
Unsupervised Video Object Segmentation On 12
評価指標
J
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
J
Paper Title
FakeFlow
75.1
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
AMP
75.0
Adaptive Multi-source Predictor for Zero-shot Video Object Segmentation
DPA
73.7
Dual Prototype Attention for Unsupervised Video Object Segmentation
TMO++ (MiT-b1, MS)
73.5
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
TMO++ (RN-101)
73.1
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
TMO++ (MiT-b1)
73.0
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
TMO (RN-101)
71.5
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
TMO (MiT-b1)
71.1
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
AMC-Net
71.1
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
AGNN
70.8
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
WCS-Net
70.5
Unsupervised Video Object Segmentation with Joint Hotspot Tracking
COSNet
70.5
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks
RTNet
70.1
Reciprocal Transformations for Unsupervised Video Object Segmentation
AGS
69.7
Learning Unsupervised Video Object Segmentation Through Visual Attention
MATNet
69.0
Motion-Attentive Transition for Zero-Shot Video Object Segmentation
PDB
65.5
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
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