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Video Salient Object Detection On Fbms 59

Métriques

AVERAGE MAE

MAX F-MEASURE

S-Measure

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	AVERAGE MAE	MAX F-MEASURE	S-Measure	Paper Title	Repository
SAGM	0.161	0.564	0.659	Saliency-Aware Geodesic Video Object Segmentation	-
FGRN	0.088	0.767	0.809	Flow Guided Recurrent Neural Encoder for Video Salient Object Detection	-
TIMP	0.192	0.465	0.576	Time-Mapping Using Space-Time Saliency	-
SSAV	0.040	0.865	0.879	Shifting More Attention to Video Salient Object Detection
MBNM	0.047	0.816	0.857	Unsupervised Video Object Segmentation with Motion-based Bilateral Networks	-
LTSI	0.087	0.799	0.805	-	-
SPD	0.125	0.686	0.691	-	-
RSE	0.128	0.652	0.670	-	-
UFO	0.028	0.890	0.894	A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
MSTM	0.177	0.500	0.613	Real-Time Salient Object Detection With a Minimum Spanning Tree
SRP	0.134	0.671	0.684	-	-
RealFlow	0.028	0.906	0.926	Transforming Static Images Using Generative Models for Video Salient Object Detection
MB+M	0.206	0.487	0.609	Minimum Barrier Salient Object Detection at 80 FPS	-
MESO	0.134	0.618	0.635	-	-
RCRNet+NER	0.054	0.861	0.870	Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
PDB	0.064	0.821	0.851	Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection	-

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