Downloads · 30 days
0
maurodrago/SurgViVQA-weights
SurgViVQA-weights is a visual question answering model from maurodrago. Use it for the visual question answering task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
license: mit pipelinetag: visual-question-answering libraryname: transformers tags: - surgical-video - video-question-answering - visual-question-answering - medical-ai --- ---
Downloads · 30 days
0
Access
Public
Updated Aug 11, 2026
Repo size
2.7 GB
Likes
0
Public
Click a slice to open those files.
.zip2.7 GB · 100%
From the Hugging Face model README
license: mit pipeline_tag: visual-question-answering library_name: transformers tags:
This repository contains the pre-trained model weights for SurgViVQA: Temporally Grounded Video Question Answering for Surgical Scene Understanding.
The weights are provided for use with the SurgViVQA project.
| File | Dataset |
|---|---|
surgvivqa_gpt2_endovis.zip | EndoVis |
surgvivqa_gpt2_realcolon.zip | RealColon |
Download the appropriate pre-trained weights and follow the instructions in the SurgViVQA repository for installation and usage.
If you use SurgViVQA in your research, please cite:
@article{Drago2026SurgViVQA,
title = {SurgViVQA: Temporally Grounded Video Question Answering for Surgical Scene Understanding},
author = {Drago, Mauro Orazio and Carlini, Luca and Balyemez, Pelinsu Celebi and Pierantozzi, Dennis and Lena, Chiara and Hassan, Cesare and Stoyanov, Danail and De Momi, Elena and Bano, Sophia and Hoque, Mobarak I.},
journal = {International Journal of Computer Assisted Radiology and Surgery},
year = {2026},
issn = {1861-6429},
doi = {10.1007/s11548-026-03695-z},
url = {https://doi.org/10.1007/s11548-026-03695-z}
}