metadata
license: mit
dataset_info:
features:
- name: qid
dtype: string
- name: video_id
dtype: string
- name: question_type
dtype: string
- name: capability
dtype: string
- name: question
dtype: string
- name: duration
dtype: string
- name: question_prompt
dtype: string
- name: answer
dtype: string
- name: youtube_url
dtype: string
splits:
- name: test_primary_oe
num_bytes: 695309
num_examples: 1000
- name: test
num_bytes: 515497
num_examples: 1000
- name: test_paraphrased_oe
num_bytes: 702625
num_examples: 1000
- name: test_correctly_led_oe
num_bytes: 719655
num_examples: 1000
- name: test_wrongly_led_oe
num_bytes: 715150
num_examples: 1000
- name: test_all
num_bytes: 3348236
num_examples: 5000
download_size: 18714323
dataset_size: 8685230
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: test_paraphrased_oe
path: data/test_paraphrased_oe-*
- split: test_correctly_led_oe
path: data/test_correctly_led_oe-*
- split: test_wrongly_led_oe
path: data/test_wrongly_led_oe-*
- split: test_all
path: data/test_all-*
- split: test_primary_oe
path: data/test_primary_oe-*
Towards Video Turing Test (Video-TT): Video Comprehension and Reasoning Benchmark with Complex Visual Narratives
Video-TT comprises 1,000 YouTube Shorts videos, each with one open-ended question and four adversarial questions that probe visual and narrative complexity.
🚀 What's New
- [2025.03] We release the benchmark!
1. Why we need a new benchmark like Video-TT?
- Sampling v.s Understanding: Current video understanding benchmark do not clearly distinguish between errors caused by insufficient frame sampling and errors caused by failures in actual video understanding. We ensure that each question in Video-TT can be well answered with uniformly sampled 80 frames where most current video model can easily handle.v
- Pursing human level video understanding: We carefully select Q&A, where human can achieve 87.5 points, but even the best model can only achieve 47.5. In comparison, without sampling issue, current video model can achieve more than 85 points on video understanding benchmark [1]
2. How to ensure the quality of Video-TT?
3. How to evaluate on Video-TT
4. Dataset summary
[1] Fu, Chaoyou, et al. "Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis." arXiv preprint arXiv:2405.21075 (2024).