ProfVLM: A Lightweight Video-Language Model for Multi-View Proficiency
  Estimation
Authors: name: Edoardo Bianchi, name: Jacopo Staiano, name: Antonio Liotta

Existing approaches to skill proficiency estimation often rely on black-box
video classifiers, ignoring multi-view context and lacking explainability. We
present ProfVLM, a compact vision-language model that reformulates this task as
generative reasoning: it jointly predicts skill level and generates expert-like
feedback from egocentric and exocentric videos. Central to our method is an
AttentiveGatedProjector that dynamically fuses multi-view features, projected
from a frozen TimeSformer backbone into a language model tuned for feedback
generation. Trained on EgoExo4D with expert commentaries, ProfVLM surpasses
state-of-the-art methods while using up to 20x fewer parameters and reducing
training time by up to 60%. Our approach not only achieves superior accuracy
across diverse activities, but also outputs natural language critiques aligned
with performance, offering transparent reasoning. These results highlight
generative vision-language modeling as a powerful new direction for skill
assessment.