Improving Object Classification using Pose Information
| Type of publication: | Idiap-RR |
| Citation: | Penedones_Idiap-RR-30-2012 |
| Number: | Idiap-RR-30-2012 |
| Year: | 2012 |
| Month: | 11 |
| Institution: | Idiap |
| Abstract: | We propose a method that exploits pose information in order to improve object classification. A lot of research has focused in other strategies, such as engineering feature extractors, trying different classifiers and even using transfer learning. Here, we use neural network architectures in a multi-task setup, whose outputs predict both the class and the camera azimuth. We investigate both Multi-layer Perceptrons and Convolutional Neural Network architectures, and achieve state-of-the-art results in the challenging NORB dataset. |
| Keywords: | |
| Projects: |
Idiap IM2 MASH |
| Authors: | |
| Added by: | [ADM] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
