---
pretty_name: POVQA Preprocessed Frames and Subtitle-Frame Alignments
language:
- en
task_categories:
- visual-question-answering
tags:
- video
- multimodal
- subtitles
- frame-alignment
---
# POVQA Preprocessed Frames and Subtitle-Frame Alignments
> Accepted to the MAR Workshop at CVPR 2026.
Paper |
Preprocessed Dataset |
Project Website
This dataset release contains the **preprocessed artifacts** used by POVQA. It is
intended to support research reproducibility without redistributing raw source
videos or raw subtitle files.
## What is included
- Preprocessed frame bundles for 12 movies and 4 pooling variants
- `KEY_FRAMES` bundles where available
- Sanitized subtitle-to-frame alignment JSON files
- Per-movie `run_summary.json` metadata
- A `manifest.json` file describing the staged release
## Release Notes
- Total staged size: approximately **5.30 GB**
- Total staged files: **133**
- Selected source size before packaging: **4.73 GB**
- Selected source file count before packaging: **341471**
## Directory Layout
Each movie has its own subdirectory:
```text
/
metadata_text_centric.json
metadata_text_centric_blend_blur_with_last_frame.json
metadata_text_centric_weighted_average.json
metadata_text_centric_weighted_average_exponential.json
metadata_text_centric_weighted_average_ramp.json
run_summary.json
KEY_FRAMES.tar
blend_blur_with_last_frame.tar
weighted_average.tar
weighted_average_exponential.tar
weighted_average_ramp.tar
```
The `.tar` archives preserve the original folder structure of the preprocessed
release and can be extracted with standard tooling.
## Notes on Data Content
- This release contains **derived, preprocessed artifacts only**.
- Raw videos and raw subtitle files are **not** included in this dataset repo.
- Subtitle credit / torrent source watermark text was scrubbed from the published
metadata JSON files while preserving timing and frame-mapping fields.
## Intended Use
This release is intended for:
- reproducibility of POVQA preprocessing outputs
- inspection of frame-selection / pooling outputs
- research on video-question-answering pipelines that consume derived frame data
## Responsible Use
Users are responsible for ensuring that their use of this dataset complies with
applicable law, platform terms, and any rights associated with the underlying
source media in their jurisdiction.
## Citation
If you use this dataset, please cite the POVQA paper:
```bibtex
@article{dahal2025povqa,
title = {POVQA: Preference-Optimized Video Question Answering with Rationales for Data Efficiency},
author = {Dahal, Ashim and Ghimire, Ankit and Murad, Saydul Akbar and Rahimi, Nick},
journal = {arXiv preprint arXiv:2510.01009},
year = {2025},
url = {https://arxiv.org/abs/2510.01009}
}
```