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.
CITATION STYLE
Silva, J., Varela, N., Mendoza-Palechor, F. E., & Lezama, O. B. P. (2021). Deep learning of robust representations for multi-instance and multi-label image classification. In Advances in Intelligent Systems and Computing (Vol. 1200 AISC, pp. 169–178). Springer. https://doi.org/10.1007/978-3-030-51859-2_16
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