Feature Upsampling On Imagenet
Métriques
Average Drop
Average Increase
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average Drop | Average Increase | Paper Title | Repository |
|---|---|---|---|---|
| Strided | 11.48 | 4.97 | Deep ViT Features as Dense Visual Descriptors | |
| FeatUp (Implicit) | 8.84 | 5.60 | FeatUp: A Model-Agnostic Framework for Features at Any Resolution | |
| FeatUp (JBU) | 9.83 | 5.24 | FeatUp: A Model-Agnostic Framework for Features at Any Resolution | |
| DIP | 10.57 | 5.16 | Deep Image Prior | |
| SAPA | 10.62 | 4.85 | SAPA: Similarity-Aware Point Affiliation for Feature Upsampling | |
| CARAFE | 10.24 | 4.96 | CARAFE: Content-Aware ReAssembly of FEatures |
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