Deep learning of robust representations for multi-instance and multi-label image classification
In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the disadvantage that a weakly supervised database is produced [9]. In the PCRY, eac...
- Autores:
-
Silva, Jesús
Varela Izquierdo, Noel
Mendoza Palechor, Fabio
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7257
- Acceso en línea:
- https://hdl.handle.net/11323/7257
https://repositorio.cuc.edu.co/
- Palabra clave:
- Deep learning
Image classification
Multi-instance
Multi-label
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
| Summary: | In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the disadvantage that a weakly supervised database is produced [9]. In the PCRY, each restaurant is represented by a set of images that share the attribute label(s) of that establishment. This paper explores the use of previously learned attribute extractors, trained in 3 different databases that are similar and complementary to the PCRY database |
|---|