Video Salient Object Detection On Fbms 59
Metriken
AVERAGE MAE
MAX F-MEASURE
S-Measure
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | AVERAGE MAE | MAX F-MEASURE | S-Measure |
|---|---|---|---|
| saliency-aware-geodesic-video-object | 0.161 | 0.564 | 0.659 |
| flow-guided-recurrent-neural-encoder-for | 0.088 | 0.767 | 0.809 |
| time-mapping-using-space-time-saliency | 0.192 | 0.465 | 0.576 |
| shifting-more-attention-to-video-salient | 0.040 | 0.865 | 0.879 |
| unsupervised-video-object-segmentation-with-1 | 0.047 | 0.816 | 0.857 |
| Modell 6 | 0.087 | 0.799 | 0.805 |
| Modell 7 | 0.125 | 0.686 | 0.691 |
| Modell 8 | 0.128 | 0.652 | 0.670 |
| a-unified-transformer-framework-for-group | 0.028 | 0.890 | 0.894 |
| real-time-salient-object-detection-with-a | 0.177 | 0.500 | 0.613 |
| Modell 11 | 0.134 | 0.671 | 0.684 |
| transforming-static-images-using-generative | 0.028 | 0.906 | 0.926 |
| minimum-barrier-salient-object-detection-at | 0.206 | 0.487 | 0.609 |
| Modell 14 | 0.134 | 0.618 | 0.635 |
| semi-supervised-video-salient-object | 0.054 | 0.861 | 0.870 |
| pyramid-dilated-deeper-convlstm-for-video | 0.064 | 0.821 | 0.851 |