3 accepted papers
Vision-language models (VLMs) are prone to object hal- lucinations, where they erroneously indicate the presence of certain objects in an image. Existing benchmarks quantify hallucinations using relatively small, labeled datasets. However, this approach is i) insufficient to assess hallucinations th…
While deep learning has led to huge progress in complex image classification tasks like ImageNet unexpected failure modes e.g. via spurious features call into question how reliably these classifiers work in the wild. Furthermore for safety-critical tasks the black-box nature of their decisions is pr…
Benchmark performance of deep learning classifiers alone is not a reliable predictor for the performance of a deployed model. In particular, if the image classifier has picked up spurious features in the training data, its predictions can fail in unexpected ways. In this paper, we develop a framewor…