3 papers
cs.CV2026
Specificity-aware reinforcement learning for fine-grained open-world classification
Samuele Angheben, Davide Berasi, Alessandro Conti +2
Classifying fine-grained visual concepts under open-world settings, i.e., without a predefined label set, demands models to be both accurate and specific. Recent reasoning Large Mu…
cs.LG2026
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…
cs.CV2025
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…