Datasets:

Kartiksh
/

fpsbench

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 (scripts/score_predictions.py). You can also submit your predictions to the 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

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. Licensed CC BY-SA 4.0 (annotations).