Name
Title | ||
|---|---|---|
Increasingly Specialized Ensemble of Convolutional Neural Networks for Fine-Grained Recognition. |
Abstract | ||
|---|---|---|
Finegrained recognition focuses on the challenging task of automatically identifying the subtle differences between similar categories. Current state-of-the-art approaches require elaborated feature learning procedures, involving tuning several hyper-parameters, or rely on expensive human annotations such as objects or parts location. In this paper we propose a simple method for fine-grained recognition that exploits a nearly cost-free attention-based focus operation to construct an ensemble of increasingly specialized Convolutional Neural Networks. Our method achieves state-of-the-art results on three of the most popular datasets used for fine-grained classification namely CUB Birds 200–2011, FGVC-Aircraft and Stanford Cars requiring minimal hyperparameter tuning and no annotations. |
Year | Venue | Field |
|---|---|---|
2018 | ICIP | Hyperparameter,Pattern recognition,Convolutional neural network,Computer science,Exploit,Feature extraction,Artificial intelligence,Feature learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Simonelli | 1 | 12 | 2.76 |
| De Natale Francesco | 2 | 262 | 40.77 |
| Stefano Messelodi | 3 | 208 | 17.13 |
| Samuel Rota Bulò | 4 | 564 | 33.69 |