  Out-of-distribution (OOD) detection is essential in autonomous driving, to
determine when learning-based components encounter unexpected inputs.
Traditional detectors typically use encoder models with fixed settings, thus
lacking effective human interaction capabilities. With the rise of large
foundation models, multimodal inputs offer the possibility of taking human
language as a latent representation, thus enabling language-defined OOD
detection. In this paper, we use the cosine similarity of image and text
representations encoded by the multimodal model CLIP as a new representation to
improve the transparency and controllability of latent encodings used for
visual anomaly detection. We compare our approach with existing pre-trained
encoders that can only produce latent representations that are meaningless from
the user's standpoint. Our experiments on realistic driving data show that the
language-based latent representation performs better than the traditional
representation of the vision encoder and helps improve the detection
performance when combined with standard representations.
