| --- |
| license: cc-by-sa-4.0 |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| - video-text-to-text |
| tags: |
| - video |
| - temporal-reasoning |
| - high-frame-rate |
| - videoqa |
| - benchmark |
| - minfps |
| pretty_name: FPS-Bench |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: fpsbench_v1.jsonl |
| --- |
| |
| # FPS-Bench |
|
|
| **A benchmark for high-frame-rate video understanding** (CVPR 2026, Carnegie |
| Mellon University). FPS-Bench is a 1,000-example multiple-choice VideoQA benchmark |
| targeting fine-grained temporal perception — questions that *cannot* be answered |
| from a handful of sparsely sampled frames. |
|
|
| It introduces **minFPS** (minimum necessary frame rate): the lowest frame rate at |
| which a human can consistently verify the answer (every question requires |
| minFPS ≥ 4), across **nine task categories** of rapid temporal phenomena. |
|
|
| - 📄 Project page: https://kartiksharma907.github.io/FPSBench/ |
| - 💻 Code & evaluation harness: https://github.com/KartikSharma907/FPSBench |
| - 🏆 Leaderboard: https://huggingface.co/spaces/Kartiksh/fpsbench-leaderboard |
|
|
| ## Scoring |
|
|
| This dataset **includes the answer key** (`question.answer` / `answer_text`), so |
| you can score and do error analysis locally with the evaluation harness in the |
| [code repo](https://github.com/KartikSharma907/FPSBench) |
| (`scripts/score_predictions.py`). You can also submit your predictions to the |
| [leaderboard](https://huggingface.co/spaces/Kartiksh/fpsbench-leaderboard) to |
| appear on the public table. |
|
|
| ## No videos are redistributed |
|
|
| The release contains **annotations only** — no videos, clips, frames, or |
| thumbnails. Each record points to a public YouTube source `url` with `clip` and |
| `temporal_certificate` time spans. Access the source videos yourself under |
| YouTube's Terms of Service, the source licenses, and your institution's policy. |
| The repo's `scripts/prepare_dataset.py` (opt-in, `--accept-source-terms`) helps |
| you fetch the exact clips locally. |
|
|
| ## Record schema |
|
|
| Each line of `fpsbench_v1.jsonl` is one example (`split: test`). Nested fields: |
|
|
| - `id`, `version`, `split` |
| - `source`: `{dataset, platform, video_id, url}` |
| - `time`: `clip_*` and `temporal_certificate_*` start/end/duration seconds (+ raw strings) |
| - `question`: `{text, type, choices{A..E}, answer, answer_text}` |
| - `temporal_requirements`: `{min_fps, min_required_frames_for_certificate, native_fps}` |
| - `categories`: `{task_category, visual_domain, visual_domain_fine, visual_subdomain, source_video_category}` |
| - `metadata`: `{original_row_id, source_dataset}` |
|
|
| The full JSON Schema is in `fpsbench_v1.schema.json`; aggregate statistics are in |
| `fpsbench_v1_stats.json`. A flattened CSV mirror is `fpsbench_v1.csv`. |
|
|
| ## Load it |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("Kartiksh/fpsbench", split="test") |
| print(ds[0]["question"]["text"]) |
| ``` |
|
|
| ## Statistics |
|
|
| 1,000 examples over 592 unique source videos; nine roughly balanced task |
| categories; minFPS mean ≈ 6.7 (min 4, max 30); clip duration mean ≈ 8.9 s. |
|
|
| ## Citation |
|
|
| See `CITATION.cff` in the [code repository](https://github.com/KartikSharma907/FPSBench). |
| Licensed CC BY-SA 4.0 (annotations). |
|
|
