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README.md

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+---
+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
+
+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
+<movie>/
+  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}
+}
+```

a_beautiful_mind/KEY_FRAMES.tar

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a_beautiful_mind/run_summary.json

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+  "movie": "a_beautiful_mind",
+  "video_path": "data/a_beautiful_mind/a_beautiful_mind.mp4",
+  "fps_mode": "seconds_window",
+  "fps_used_or_native": 23.976023976023978,
+  "target_fps_arg": 3.0,
+  "chunk_size_arg": 3,
+  "chunk_seconds": 1.0,
+  "skip_frames_between_chunks": 0,
+  "resize_to": [
+    224,
+    224
+  ],
+  "avg_method": "weighted_average_ramp",
+  "total_chunks": 8111,
+  "frames_processed": 194662,
+  "has_subtitles": true,
+  "key_frames_found": 0,
+  "key_frames_total_in_annotations": 0,
+  "annotation_file": null,
+  "output_dir": "out_preprocessed/a_beautiful_mind"
+}

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