4 papers · 1 filter
Synthetic Stimuli, Real Gains: Rethinking VLM Fine-Tuning Through Fully Controlled Data Generation
Massimo Rizzoli, Simone Alghisi, Seyed Mahed Mousavi +1
Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of real-world scenes. Despite the improvemen…
Getting to the Point: Pointing Improves LVLMs at Counting
Simone Alghisi, Massimo Rizzoli, Seyed Mahed Mousavi +1
Pointing-based methods decompose complex tasks as sequential grounding and reasoning steps. Given a query, the model first grounds the relevant objects by generating their coordina…
[De|Re]constructing VLMs' Reasoning in Counting
Simone Alghisi, Gabriel Roccabruna, Massimo Rizzoli +2
Vision-Language Models (VLMs) have recently gained attention due to their competitive performance on multiple downstream tasks, achieved by following user-input instructions. Howev…
CIVET: Systematic Evaluation of Understanding in VLMs
Massimo Rizzoli, Simone Alghisi, Olha Khomyn +3
While Vision-Language Models (VLMs) have achieved competitive performance in various tasks, their comprehension of the underlying structure and semantics of a scene remains underst…