36 citations · 57 across the 22 of their papers we have counts for
8 papers · 1 filter
Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith +2
Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major b…
Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs
Sanghwan Kim, Rui Xiao, Stephan Alaniz +2
Multimodal Large Language Models (MLLMs) often struggle with fine-grained perception, such as identifying small objects in high-resolution images or detecting key moments in long v…
SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions
Jessica Bader, Leander Girrbach, Stephan Alaniz +1
Concept Bottleneck Models (CBMs) and other concept-based interpretable models show great promise for making AI applications more transparent, which is essential in fields like medi…
LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance
Jae Myung Kim, Stephan Alaniz, Cordelia Schmid +1
Despite recent advances in text-to-image generation, using synthetically generated data seldom brings a significant boost in performance for supervised learning. Oftentimes, synthe…
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning
Jae Myung Kim, Stephan Alaniz, Cordelia Schmid +1
Humans can easily tell if an attribute (also called state) is realistic, i.e., feasible, for an object, e.g. fire can be hot, but it cannot be wet. In Open-World Compositional Zero…
Concept-Guided Interpretability via Neural Chunking
Shuchen Wu, Stephan Alaniz, Shyamgopal Karthik +3
Neural networks are often described as black boxes, reflecting the significant challenge of understanding their internal workings and interactions. We propose a different perspecti…
A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith +1
With the increasing use of image generation technology, understanding its social biases, including gender bias, is essential. This paper presents a large-scale study on gender bias…
Discovering Chunks in Neural Embeddings for Interpretability
Shuchen Wu, Stephan Alaniz, Eric Schulz +1
Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it…