Name
Abstract | ||
|---|---|---|
This paper shows how state-of-the-art deep learning methods can be combined to successfully tackle a new classification task related to chairlift security using visual information. In particular, we show that with an effective architecture and some domain adaptation components, we can learn an end-to-end model that could be deployed in ski resorts to improve the security of chairlift passengers. Our experiments show that our method gives better results than already deployed hand-tuned systems when using all the available data and very promising results on new unseen chairlifts. |
Year | Venue | Field |
|---|---|---|
2017 | IDA | Architecture,Computer science,Domain adaptation,Convolutional neural network,Artificial intelligence,Deep learning,Contextual image classification,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Bascol | 1 | 0 | 0.34 |
| Rémi Emonet | 2 | 61 | 7.60 |
| Élisa Fromont | 3 | 192 | 25.51 |
| Raluca Debusschere | 4 | 0 | 0.34 |