README.md

---
license: cc-by-4.0
language:
- en
---


# Dataset Card for TimeIT

TimeIT encompasses 6 longstanding timestamp-related video tasks and incorporates 12 specific datasets derived from different domains.

## Dataset Description


- **Homepage: https://huggingface.co/datasets/ShuhuaiRen/TimeIT** 
- **Repository: https://huggingface.co/datasets/ShuhuaiRen/TimeIT** 
- **Paper: https://arxiv.org/abs/2312.02051** 
- **Leaderboard:** 
- **Point of Contact:** 

## Dataset Statistics

Our dataset compiles diverse tasks of time-sensitive long video understanding, including Dense Video Captioning, Video Grounding, Video Summarization, Video Highlight Detection, Step Localization, Transcribed Speech Generation.

### Instruction Statistics

| Task                          | #Instructions |
|-------------------------------|---------------|
| Dense Video Captioning        | 6             |
| Temporal Video Grounding      | 6             |
| Video Summarization           | 6             |
| Video Highlight Detection     | 6             |
| Step Localization             | 6             |
| Transcribed Speech Generation | 6             |
| Total                         | 36            |

### Task Statistics

| Task                          | Description                                                                                                          | #Train  |
|-------------------------------|----------------------------------------------------------------------------------------------------------------------|---------|
| Dense Video Captioning        | detects a series of events in the given video and outputs the corresponding timestamps and descriptions              | 16,342  |
| Temporal Video Grounding      | predict a timestamp boundary including the start and end time in the video given a natural language query            | 60,471  |
| Video Summarization           | create a compressed set of frames or clip shots to represent the most informative content of the given video         | 75      |
| Video Highlight Detection     | identify the most exciting, impressive, or emotional moments that may not cover the full scope of the original video | 6,858   |
| Step Localization             | segment and describe significant steps in a long untrimmed video                                                     | 9,488   |
| Transcribed Speech Generation | predict the speech content and its corresponding start and end timestamps based on visual signals in the video       | 31,627  |
| Total                         | -                                                                                                                    | 124861  |

### Detailed Dataset Statistics

| Task                          | Dataset                | #Train |
|-------------------------------|------------------------|--------|
| Dense Video Captioning        | `ActivityNet Captions` | 10,009 |
|                               | `ViTT`                 | 5,141  |
|                               | `YouCook2`             | 1,192  |
| Temporal Video Grounding      | `DiDeMo`               | 33,002 |
|                               | `QuerYD`               | 14,602 |
|                               | `HiREST_grounding`     | 459    |
|                               | `Charades-STA`         | 12,408 |
| Video Summarization           | `TVSum`                | 50     |
|                               | `SumMe`                | 25     |
| Video Highlight Detection     | `QVHighlights`         | 6,858  |
| Step Localization             | `COIN`                 | 9,029  |
|                               | `HiREST_step`          | 459    |
| Transcribed Speech Generation | `YT-Temporal`          | 31,627 |

## Dataset Structure

### HuggingFace Login (Optional)

```python
# OR run huggingface-cli login
from huggingface_hub import login

hf_token = "hf_xxx"  # TODO: set a valid HuggingFace access token for loading datasets/models
login(token=hf_token)
```

### Data Loading

```python
from datasets import load_dataset

ds_name = "youcook2"  # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)
```

### Data Splits

```python
from datasets import load_dataset

ds_name = "youcook2"  # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)
train_set = dataset["train"]
```

### Data Instances

```python
from datasets import load_dataset
from io import BytesIO
from base64 import b64decode
from PIL import Image

ds_name = "youcook2"  # change the dataset name here
dataset = load_dataset("ShuhuaiRen/TimeIT", ds_name)
train_set = dataset["train"]

for train_instance in train_set:
    question = train_instance["question"]  # str
    answer = train_instance["answer"]  # str
    video_path = train_instance["video_path"]  # str
```

### Data Fields

```python
import datasets

features = datasets.Features(
    {
        "video_path": datasets.Value("string"),
        "question": datasets.Value("string"),
        "answer": datasets.Value("string"),
    }
)
```

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

| Task                          | Dataset [Citation]         | Source                                                                         |
|-------------------------------|----------------------------|--------------------------------------------------------------------------------|
| Dense Video Captioning        | `ActivityNet Captions` [1] | [Source](https://cs.stanford.edu/people/ranjaykrishna/densevid/)               |
|                               | `ViTT` [2]                 | [Source](https://github.com/google-research-datasets/Video-Timeline-Tags-ViTT) |
|                               | `YouCook2` [3]             | [Source](http://youcook2.eecs.umich.edu/)                                      |
| Temporal Video Grounding      | `DiDeMo` [4]               | [Source](https://github.com/LisaAnne/TemporalLanguageRelease)                  |
|                               | `QuerYD` [5]               | [Source](https://www.robots.ox.ac.uk/~vgg/data/queryd/)                        |
|                               | `HiREST_grounding` [6]     | [Source](https://hirest-cvpr2023.github.io/)                                   |
|                               | `Charades-STA` [7]         | [Source](https://github.com/jiyanggao/TALL)                                    |
| Video Summarization           | `TVSum` [8]                | [Source](https://github.com/yalesong/tvsum)                                    |
|                               | `SumMe` [9]                | [Source](http://classif.ai/dataset/ethz-cvl-video-summe/)                      |
| Video Highlight Detection     | `QVHighlights` [10]        | [Source](https://github.com/jayleicn/moment_detr/tree/main/data)               |
| Step Localization             | `COIN` [11]                | [Source](https://coin-dataset.github.io/)                                      |
|                               | `HiREST_step` [6]          | [Source](https://hirest-cvpr2023.github.io/)                                   |
| Transcribed Speech Generation | `YT-Temporal` [12]         | [Source](https://rowanzellers.com/merlot/#data)                                |

### Annotations

#### Annotation process

To build high-quality multimodal instruction datasets, 
we rewrite various datasets into multimodal-to-text dialog format. 
The annotation process includes four steps:

- (1) **Stage I: Instruction Writing**: writing instructions for each task;
- (2) **Stage II: Data Format Unification**: structuring images and texts into a unified schema;
- (3) **Stage III: Quality Check**: checking the overall dataset quality;
- (4) **Stage IV: Key Datasets Translation**: building multilingual sets. 

#### Who are the annotators?

Three authors of this work are employed as human annotators, 
each of whom is a graduate student familiar with relevant literature.


## Additional Information

### Licensing Information

The content of original dataset follows their original license. 
We suggest that for the task with Unknown/Custom license, the user can check the original project or contact the dataset owner for detailed license information.

Our annotated instruction data is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).


### Citation Information
```bibtex
@article{Ren2023TimeChatAT,
  title={TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding},
  author={Shuhuai Ren and Linli Yao and Shicheng Li and Xu Sun and Lu Hou},
  journal={ArXiv},
  year={2023},
  volume={abs/2312.02051},
}
```
### Contributions

TimeIT is a video-centric instruction-tuning dataset involving timestamps.
designed to enable the development of general-purpose video agents.

## References

- [1] Dense-Captioning Events in Videos
- [2] Multimodal Pretraining for Dense Video Captioning
- [3] Towards Automatic Learning of Procedures from Web Instructional Videos
- [4] Localizing Moments in Video with Natural Language
- [5] QuerYD: A video dataset with high-quality text and audio narrations
- [6] Hierarchical Video-Moment Retrieval and Step-Captioning
- [7] TALL: Temporal Activity Localization via Language Query
- [8] TVSum: Summarizing Web Videos Using Titles
- [9] Creating Summaries from User Videos
- [10] QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries
- [11] COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
- [12] MERLOT: Multimodal Neural Script Knowledge Models