1 citations · 1 across the 8 of their papers we have counts for
8 papers
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen +33
Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…
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…
Concept-Aware Batch Sampling Improves Language-Image Pretraining
Adhiraj Ghosh, Vishaal Udandarao, Thao Nguyen +7
What data should a vision-language model be trained on? To answer this question, many data curation efforts center on the quality of a dataset. However, most of these existing meth…
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…
Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages
Matteo Farina, Massimiliano Mancini, Giovanni Iacca +1
An old-school recipe for training a classifier is to (i) learn a good feature extractor and (ii) optimize a linear layer atop. When only a handful of samples are available per cate…
Frustratingly Easy Test-Time Adaptation of Vision-Language Models
Matteo Farina, Gianni Franchi, Giovanni Iacca +2
Vision-Language Models seamlessly discriminate among arbitrary semantic categories, yet they still suffer from poor generalization when presented with challenging examples. For thi…
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
Matteo Farina, Massimiliano Mancini, Elia Cunegatti +3
While excellent in transfer learning, Vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing para…