4 accepted papers
Recent advances in vision-language models (VLMs) have achieved remarkable performance on standard medical benchmarks, yet their true clinical reasoning ability remains unclear. Existing datasets predominantly emphasize classification accuracy, creating an evaluation illusion in which models appear p…
The uncertainty of an automatic speaker verification (ASV) system is typically estimated using its overall accuracy. However it fails to express "when" the system is uncertain in a predictive and case-by-case manner. Also, prior to interpreting each prediction made by ASV systems, there is a need to…
In out-of-domain (OOD) detection tasks, encoding the actual data into a suitable latent space could be beneficial since it may facilitate measurement of the spatial relationship between in-domain (IND) and OOD data. However, any such mapping of data to a latent space carries the risk that some OOD p…
In machine learning applications, it is desirable to distinguish between in-domain and out-of-domain data. However, in most cases, only in-domain data is available and consequently identifying the ‘boundary’ between in-domain and out-of-domain is a significant challenge. In this paper we present a n…