Video Salient Object Detection On Visal
Métriques
Average MAE
S-Measure
max E-measure
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Average MAE | S-Measure | max E-measure |
|---|---|---|---|
| saliency-aware-geodesic-video-object | 0.105 | 0.749 | 0.858 |
| shifting-more-attention-to-video-salient | 0.021 | 0.942 | 0.980 |
| unsupervised-video-object-segmentation-with-1 | 0.047 | 0.857 | 0.892 |
| flow-guided-recurrent-neural-encoder-for | 0.045 | 0.861 | 0.945 |
| real-time-salient-object-detection-with-a | 0.095 | 0.749 | 0.816 |
| a-unified-transformer-framework-for-group | 0.011 | 0.953 | 0.987 |
| transforming-static-images-using-generative | 0.010 | 0.962 | 0.966 |
| minimum-barrier-salient-object-detection-at | 0.129 | 0.726 | 0.832 |
| time-mapping-using-space-time-saliency | 0.170 | 0.612 | 0.743 |
| pyramid-dilated-deeper-convlstm-for-video | 0.032 | 0.907 | 0.846 |