1 citations · 1 across the 6 of their papers we have counts for
11 papers
SEM: Sparse Embedding Modulation for Post-Hoc Debiasing of Vision-Language Models
Quentin Guimard, Federico Bartsch, Simone Caldarella +3
Models that bridge vision and language, such as CLIP, are key components of multimodal AI, yet their large-scale, uncurated training data introduce severe social and spurious biase…
Large Multimodal Models as General In-Context Classifiers
Marco Garosi, Matteo Farina, Alessandro Conti +2
Which multimodal model should we use for classification? Previous studies suggest that the answer lies in CLIP-like contrastive Vision-Language Models (VLMs), due to their remarkab…
Linear Model Merging Unlocks Simple and Scalable Multimodal Data Mixture Optimization
Davide Berasi, Matteo Farina, Massimiliano Mancini +1
Selecting the best data mixture is critical for successful Supervised Fine-Tuning (SFT) of Multimodal Large Language Models. However, determining the optimal mixture weights across…
Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers
Quentin Guimard, Moreno D'Incà, Massimiliano Mancini +1
A person downloading a pre-trained model from the web should be aware of its biases. Existing approaches for bias identification rely on datasets containing labels for the task of…
Compositional Caching for Training-free Open-vocabulary Attribute Detection
Marco Garosi, Alessandro Conti, Gaowen Liu +2
Attribute detection is crucial for many computer vision tasks, as it enables systems to describe properties such as color, texture, and material. Current approaches often rely on l…
Not Only Text: Exploring Compositionality of Visual Representations in Vision-Language Models
Davide Berasi, Matteo Farina, Massimiliano Mancini +2
Vision-Language Models (VLMs) learn a shared feature space for text and images, enabling the comparison of inputs of different modalities. While prior works demonstrated that VLMs…