---
license: apache-2.0
dataset_info:
  features:
  - name: video_name
    dtype: string
  - name: idx
    dtype: string
  - name: question
    dtype: string
  - name: GT
    dtype: string
  - name: dataset
    dtype: string
  - name: category
    dtype: string
  splits:
  - name: test
    num_bytes: 7347835
    num_examples: 9867
  - name: test_short_qa
    num_bytes: 7347835
    num_examples: 9867
  - name: test_long_qa
    num_bytes: 8468396
    num_examples: 5485
  - name: test_short_caption
    num_bytes: 2296752
    num_examples: 1891
  download_size: 2997558
  dataset_size: 25460818
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
  - split: test_short_qa
    path: data/test_short_qa-*
  - split: test_long_qa
    path: data/test_long_qa-*
  - split: test_short_caption
    path: data/test_short_caption-*
task_categories:
- video-text-to-text
language:
- en
tags:
- temporal-reasoning
size_categories:
- 1K<n<10K
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---

# Dataset Card

**Dataset is released now!**
<!-- insert an figure here -->
![Alt text](https://temporalbench.github.io/static/images/teaser-enlarged-v1.png)
[[Project Page]](https://temporalbench.github.io/) [[arXiv]](https://arxiv.org/abs/2410.10818) [[code]](https://github.com/mu-cai/TemporalBench) [[Leaderboard]](https://temporalbench.github.io/#leaderboard)

**TemporalBench** is a video understanding benchmark designed to evaluate fine-grained temporal reasoning for multimodal video models. It consists of ∼10K video question-answer pairs sourced from ∼2K high-quality human-annotated video captions, capturing detailed temporal dynamics and actions.

### Dataset Description

- **Curated by:** Mu Cai, Reuben Tan, Jianfeng Gao, Yong Jae Lee, Jianwei Yang, etc.
- **Language(s):** English
- **License:** MIT

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Paper:** [TemporalBench: Benchmarking Fine-Grained Temporal Understanding for Multimodal Video Models](https://arxiv.org/abs/2410.10818)

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

The dataset is useful for assessing the temporal reasoning abilities of multimodal models, particularly fine-grained video understanding, long-term video understanding, and video captioning with rich details.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

The dataset is not intended for use in non-temporal video tasks.

## Dataset Structure

### Data Instances

Each data instance in the dataset consists of a question-answer pair based on a video clip. Below is an example from the dataset:

```python
{
    "idx": "short_video/Charades/EEVD3_start_11.4_end_16.9.mp4_0",
    "video_name": "short_video/Charades/EEVD3_start_11.4_end_16.9.mp4",
    "category": "Action Effector",
    "source_dataset": "Charades",
    "question": "Which caption best describes this video?\nA. A person closes the door of the fridge with his left hand while looking at the bowl of fruit he holds in his right hand. He transfers the bowl from his right hand to his left hand. He picks up a fruit from the bowl with his left hand. He tosses the fruit up with his left hand and catches it with the same hand while walking forward. \nB. A person closes the door of the fridge with his left hand while looking at the bowl of fruit he holds in his right hand. He transfers the bowl from his right hand to his left hand. He picks up a fruit from the bowl with his right hand. He tosses the fruit up with his right hand and catches it with the same hand while walking forward.\nAnswer with the option's letter from the given choices directly.",
    "GT": "B"
}
```

### Data Fields

- `idx`: A string representing the video identifier.
- `video_name`: Video path
- `question`: A string containing the question related to the video.
- `GT`: A string containing the correct answer.

### Data Splits

The dataset is split into the following:

- `test`: 9867 rows


## Dataset Creation

This dataset was created from human annotators with fine-grained temporal annotations. The videos were sampled from various sources, including procedural videos and human activities.

### Source Data

- ActivityNet-Captions, COIN, Charades-STA, FineGym, Oops, Movei Description, EgoExo4d, 

#### Data Collection and Processing

Refer to the main paper for detailed information about the data collection and curation process.

#### Personal and Sensitive Information

No personal or sensitive information is contained in this dataset.

## Bias, Risks, and Limitations

TemporalBench is made for academic research purposes only. Commercial use in any form is strictly prohibited.
The copyright of all videos belong to their respective owners. We do not own any of the videos.
Any form of unauthorized distribution, publication, copying, dissemination, or modifications made over TemporalBench in part or in whole is strictly prohibited.
You cannot access our dataset unless you comply to all the above restrictions and also provide your information for legal purposes.
This dataset is foucsing on fine-grained temporal tasks rather than coarse-grained video understanding.

## Citation

If you find this work useful, please cite:

```
@article{cai2024temporalbench,
      title={TemporalBench: Towards Fine-grained Temporal Understanding for Multimodal Video Models},
      author={Cai, Mu and Tan, Reuben and Zhang, Jianrui and Zou, Bocheng and Zhang, Kai and Yao, Feng and Zhu, Fangrui and Gu, Jing and Zhong, Yiwu and Shang, Yuzhang and Dou, Yao and Park, Jaden and Gao, Jianfeng and Lee, Yong Jae and Yang, Jianwei},
      journal={arXiv preprint arXiv:2410.10818},
      year={2024}
}
```