34 citations · 140 across the 26 of their papers we have counts for
7 papers · 1 filter
ActivationReasoning: Logical Reasoning in Latent Activation Spaces
Lukas Helff, Ruben Härle, Wolfgang Stammer +6
Large language models (LLMs) excel at generating fluent text, but their internal reasoning remains opaque and difficult to control. Sparse autoencoders (SAEs) make hidden activatio…
DeiSAM: Segment Anything with Deictic Prompting
Hikaru Shindo, Manuel Brack, Gopika Sudhakaran +3
Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex sce…
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
Lukas Struppek, Dominik Hintersdorf, Felix Friedrich +3
Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising conc…
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Felix Friedrich, Manuel Brack, Lukas Struppek +4
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly da…
AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
Björn Deiseroth, Mayukh Deb, Samuel Weinbach +3
Generative transformer models have become increasingly complex, with large numbers of parameters and the ability to process multiple input modalities. Current methods for explainin…
Does CLIP Know My Face?
Dominik Hintersdorf, Lukas Struppek, Manuel Brack +3
With the rise of deep learning in various applications, privacy concerns around the protection of training data have become a critical area of research. Whereas prior studies have…
ILLUME: Rationalizing Vision-Language Models through Human Interactions
Manuel Brack, Patrick Schramowski, Björn Deiseroth +1
Bootstrapping from pre-trained language models has been proven to be an efficient approach for building vision-language models (VLM) for tasks such as image captioning or visual qu…