  In this paper we deal with the task of Disturbing Image Detection (DID),
exploiting knowledge encoded in Large Multimodal Models (LMMs). Specifically,
we propose to exploit LMM knowledge in a two-fold manner: first by extracting
generic semantic descriptions, and second by extracting elicited emotions.
Subsequently, we use the CLIP's text encoder in order to obtain the text
embeddings of both the generic semantic descriptions and LMM-elicited emotions.
Finally, we use the aforementioned text embeddings along with the corresponding
CLIP's image embeddings for performing the DID task. The proposed method
significantly improves the baseline classification accuracy, achieving
state-of-the-art performance on the augmented Disturbing Image Detection
dataset.
