Accurate documentation of newborn resuscitation is essential for quality improvement and adherence to clinical guidelines, yet remains underutilized in practice. Previous work using 3D-CNNs and Vision Transformers (ViT) has shown promising results in detecting key activities from newborn resuscitation videos, but also highlighted the challenges in recognizing such fine-grained activities. This work investigates the potential of generative AI (GenAI) methods to improve activity recognition from such videos. Specifically, we explore the use of local vision-language models (VLMs), combined with large language models (LLMs), and compare them to a supervised TimeSFormer baseline. Using a simulated dataset comprising 13.26 hours of newborn resuscitation videos, we evaluate several zero-shot VLM-based strategies and fine-tuned VLMs with classification heads, including Low-Rank Adaptation (LoRA). Our results suggest that small (local) VLMs struggle with hallucinations, but when fine-tuned with LoRA, the results reach F1 score at 0.91, surpassing the TimeSformer results of 0.70.
@article{arxiv.2602.12002,
title = {Can Local Vision-Language Models improve Activity Recognition over Vision Transformers? -- Case Study on Newborn Resuscitation},
author = {Enrico Guerriero and Kjersti Engan and Øyvind Meinich-Bache},
journal= {arXiv preprint arXiv:2602.12002},
year = {2026}
}
Comments
Presented at the Satellite Workshop on Workshop 15: Generative AI for World Simulations and Communications & Celebrating 40 Years of Excellence in Education: Honoring Professor Aggelos Katsaggelos, IEEE International Conference on Image Processing (ICIP), 2025