5 accepted papers
Large vision-language models (LVLMs) have shown substantial advances in multimodal understanding and generation. However, when presented with incompetent or adversarial inputs, they frequently produce unreliable or even harmful contents, such as fact hallucinations or dangerous instructions. This mi…
Concept Bottleneck Models (CBMs) enhance interpretability by explaining predictions through human-understandable concepts but typically assume that training and test data share the same distribution. This assumption often fails under domain shifts, leading to degraded performance and poor generaliza…
While the Segment Anything Model (SAM) transforms interactive segmentation with zero-shot abilities, its inherent vulnerabilities present a single-point risk, potentially leading to the failure of downstream applications. Proactively evaluating these transferable vulnerabilities is thus imperative.…
Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts. However,…
Deep architectures have become ubiquitous in Music Information Retrieval (MIR) tasks, however, concurrent studies still lack a deep understanding of the input properties being evaluated by the networks. In this study, we show by the example of a Music Genre Classification system the potential depend…