Document - Multimodal heterogeneous transfer learning for multilingual image-text classification

2024

Conference article Open Access

Multimodal heterogeneous transfer learning for multilingual image-text classification

Pedrotti A., Moreo Fernandez A., Sebastiani F.

Heterogeneous transfer learning Multilingual classification Multimodal classification

The Multilingual Image-Text Classification (MITC) task is a specific instance of the Image-Text Classification (ITC) task, where each item to be classified consists of a visual representation and a textual description written in one of several possible languages. In this paper we propose MM-gFun, an extension of the gFun learning architecture originally developed for cross-lingual text classification. We extend its original text-only implementation to handle perceptual modalities.

Source: CEUR WORKSHOP PROCEEDINGS, vol. 3928. Pisa, Italy, 14-16/10/2024

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@inproceedings{oai:iris.cnr.it:20.500.14243/537926,
	title = {Multimodal heterogeneous transfer learning for multilingual image-text classification},
	author = {Pedrotti A. and Moreo Fernandez A. and Sebastiani F.},
	booktitle = {CEUR WORKSHOP PROCEEDINGS, vol. 3928. Pisa, Italy, 14-16/10/2024},
	year = {2024}
}