2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
RePOPE: Impact of Annotation Errors on the POPE Benchmark
Yannic Neuhaus, Matthias Hein
Since data annotation is costly, benchmark datasets often incorporate labels from established image datasets. In this work, we assess the impact of label errors in MSCOCO on the fr…
DASH: Detection and Assessment of Systematic Hallucinations of VLMs
Maximilian Augustin, Yannic Neuhaus, Matthias Hein
Vision-language models (VLMs) are prone to object hallucinations, where they erroneously indicate the presenceof certain objects in an image. Existing benchmarks quantify hallucina…
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations
Maximilian Augustin, Yannic Neuhaus, Matthias Hein
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 relia…
Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko +1
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 s…