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One of the main issues related to unsupervised machine learning is the costof processing and extracting useful information from large datasets. In thiswork, we propose a classifier ensemble based on the transferable learningcapabilities of the CLIP neural network architecture in multimodal environments(image and text) from social media. For this purpose, we used the InstaNY100Kdataset and proposed a validation approach based on sampling techniques. Ourexperiments, based on image classification tasks according to the labels of thePlaces dataset, are performed by first considering only the visual part, andthen adding the associated texts as support. The results obtained demonstratedthat trained neural networks such as CLIP can be successfully applied to imageclassification with little fine-tuning, and considering the associated texts tothe images can help to improve the accuracy depending on the goal. The resultsdemonstrated what seems to be a promising research direction.

Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learning