Name
Abstract | ||
|---|---|---|
In this paper we present a method for still image memorability estimation. The proposed solution exploits feature maps extracted from two Convolutional Neural Networks pre-trained for object recognition and memorability estimation respectively. The feature maps are then enhanced using a soft attention mechanism in order to let the model focus on highly informative image regions for memorability estimation. Results achieved on a benchmark dataset demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30645-8_16 | IMAGE ANALYSIS AND PROCESSING - ICIAP 2019, PT II |
Keywords | DocType | Volume |
Memorability, Residual Neural Network, Convolutional Neural Network, Deep learning | Conference | 11752 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Leonardi | 1 | 1 | 0.69 |
| Luigi Celona | 2 | 66 | 7.70 |
| Paolo Napoletano | 3 | 339 | 37.19 |
| Simone Bianco | 4 | 226 | 24.48 |
| Raimondo Schettini | 5 | 1476 | 154.06 |
| Franco Manessi | 6 | 23 | 2.35 |
| Alessandro Rozza | 7 | 0 | 0.34 |