Unsupervised Video Object Segmentation On 12

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		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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