  In this paper we address image classification tasks leveraging knowledge
encoded in Large Multimodal Models (LMMs). More specifically, we use the
MiniGPT-4 model to extract semantic descriptions for the images, in a
multimodal prompting fashion. In the current literature, vision language models
such as CLIP, among other approaches, are utilized as feature extractors, using
only the image encoder, for solving image classification tasks. In this paper,
we propose to additionally use the text encoder to obtain the text embeddings
corresponding to the MiniGPT-4-generated semantic descriptions. Thus, we use
both the image and text embeddings for solving the image classification task.
The experimental evaluation on three datasets validates the improved
classification performance achieved by exploiting LMM-based knowledge.
