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Video-Salientes-Objekt-Erkennung
Video Salient Object Detection On Fbms 59
Video Salient Object Detection On Fbms 59
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AVERAGE MAE
MAX F-MEASURE
S-Measure
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
AVERAGE MAE
MAX F-MEASURE
S-Measure
Paper Title
MB+M
0.206
0.487
0.609
Minimum Barrier Salient Object Detection at 80 FPS
TIMP
0.192
0.465
0.576
Time-Mapping Using Space-Time Saliency
MSTM
0.177
0.500
0.613
Real-Time Salient Object Detection With a Minimum Spanning Tree
SAGM
0.161
0.564
0.659
Saliency-Aware Geodesic Video Object Segmentation
SRP
0.134
0.671
0.684
-
MESO
0.134
0.618
0.635
-
RSE
0.128
0.652
0.670
-
SPD
0.125
0.686
0.691
-
FGRN
0.088
0.767
0.809
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection
LTSI
0.087
0.799
0.805
-
PDB
0.064
0.821
0.851
Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection
RCRNet+NER
0.054
0.861
0.870
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
MBNM
0.047
0.816
0.857
Unsupervised Video Object Segmentation with Motion-based Bilateral Networks
SSAV
0.040
0.865
0.879
Shifting More Attention to Video Salient Object Detection
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
RealFlow
0.028
0.906
0.926
Transforming Static Images Using Generative Models for Video Salient Object Detection
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