8 papers · 1 filter
VETO: Towards Protecting Images From Frontier AI Editing
Jonas Grebe, Hossein Shakibania, Tobias Braun +2
The rise of powerful, accessible image-editing models such as FLUX.2 has brought high-fidelity editing within broad reach. Their capabilities now extend beyond localized modificati…
Obliviate: Erasing Concepts from Autoregressive Image Generation Models
Hossein Shakibania, Jonas Henry Grebe, Tobias Braun +4
The widespread adoption of generative AI models has intensified concerns about misuse, including the creation of unsafe or disturbing imagery. To mitigate such issues, several conc…
SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Hector G. Rodriguez, Marcus Rohrbach
Multimodal large language models (MLLMs) achieve ever-stronger performance on visual-language tasks. Even as traditional visual question answering (VQA) benchmarks approach saturat…
HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
Reihaneh Zohrabi, Hosein Hasani, Akshita Gupta +3
Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly r…
Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
Reihaneh Zohrabi, Hosein Hasani, Mahdieh Soleymani Baghshah +3
Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications, where they frequently face data distri…
Variational Visual Question Answering for Uncertainty-Aware Selective Prediction
Tobias Jan Wieczorek, Nathalie Daun, Mohammad Emtiyaz Khan +1
Despite remarkable progress in recent years, Vision Language Models (VLMs) remain prone to overconfidence and hallucinations on tasks such as Visual Question Answering (VQA) and Vi…
VDial: Unification of Video and Visual Dialog via Multimodal Experts
Adnen Abdessaied, Anna Rohrbach, Marcus Rohrbach +1
We present VDial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks. Current multim…
DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Tobias Braun, Mark Rothermel, Marcus Rohrbach +1
The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a modul…