Video Salient Object Detection On Davsod
Metrics
Average MAE
S-Measure
max E-Measure
Results
Performance results of various models on this benchmark
Model Name | Average MAE | S-Measure | max E-Measure | Paper Title | Repository |
|---|---|---|---|---|---|
| TIMP | 0.206 | 0.534 | 0.582 | Time-Mapping Using Space-Time Saliency | - |
| SSAV | 0.084 | 0.755 | 0.806 | Shifting More Attention to Video Salient Object Detection | |
| MBNM | 0.109 | 0.646 | 0.694 | Unsupervised Video Object Segmentation with Motion-based Bilateral Networks | - |
| FGRN | 0.095 | 0.701 | 0.765 | Flow Guided Recurrent Neural Encoder for Video Salient Object Detection | - |
| SAGM | 0.187 | 0.564 | 0.640 | Saliency-Aware Geodesic Video Object Segmentation | - |
| RealFlow | 0.066 | 0.803 | - | Transforming Static Images Using Generative Models for Video Salient Object Detection | |
| PDB | 0.114 | 0.706 | 0.749 | Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection | - |
| MB+M | 0.231 | 0.536 | 0.624 | Minimum Barrier Salient Object Detection at 80 FPS | - |
| MSTM | 0.214 | 0.530 | 0.632 | Real-Time Salient Object Detection With a Minimum Spanning Tree |
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