Video Salient Object Detection On Davis 2016
Métriques
AVERAGE MAE
MAX F-MEASURE
S-Measure
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AVERAGE MAE | MAX F-MEASURE | S-Measure |
|---|---|---|---|
| semi-supervised-video-salient-object | 0.028 | 0.859 | 0.884 |
| saliency-aware-geodesic-video-object | 0.105 | - | 0.664 |
| a-unified-transformer-framework-for-group | 0.015 | 0.906 | 0.918 |
| real-time-salient-object-detection-with-a | 0.174 | - | 0.566 |
| minimum-barrier-salient-object-detection-at | 0.173 | - | 0.600 |
| time-mapping-using-space-time-saliency | 0.185 | - | 0.574 |
| unsupervised-video-object-segmentation-with-1 | 0.031 | 0.862 | 0.887 |
| flow-guided-recurrent-neural-encoder-for | 0.043 | 0.783 | 0.838 |
| transforming-static-images-using-generative | 0.010 | 0.939 | 0.945 |
| pyramid-dilated-deeper-convlstm-for-video | 0.028 | - | 0.882 |
| shifting-more-attention-to-video-salient | 0.028 | 0.861 | 0.893 |