README.md

---
pretty_name: GLaVE-1.2M
license: other
license_name: academic-non-commercial-use-only
license_link: https://github.com/HIT-leaderone/GLaVE-Cap
language:
  - en
task_categories:
  - visual-question-answering
size_categories:
  - 10K<n<100K
tags:
  - video
  - video-captioning
  - fine-grained-video-understanding
  - parquet
configs:
  - config_name: raw
    data_files:
      - split: train
        path: raw/*.parquet
---

# GLaVE-1.2M

GLaVE-1.2M is the training dataset released with
**GLaVE-Cap: Global-Local Aligned Video Captioning with Vision Expert Integration**.
It contains fine-grained video captions, intermediate local annotations, scene-level
annotations, and multiple-choice question-answer pairs for video understanding.

This repository contains **annotations only**. It does not redistribute the original
video files. The original videos are available from
[lmms-lab/LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K).
The `data_source` and `video_source` columns identify the corresponding source video.

- Paper: [arXiv:2509.11360](https://arxiv.org/abs/2509.11360)
- Code: [HIT-leaderone/GLaVE-Cap](https://github.com/HIT-leaderone/GLaVE-Cap)

## Dataset Summary

| Item | Value |
| --- | ---: |
| Video-level records | 19,702 |
| Raw Parquet shards | 20 |
| Videos per shard | 1,000, except the final shard with 702 |
| Raw Parquet size | 1,105,941,274 bytes (about 1.106 GB / 1.030 GiB) |
| QA rows in the flattened QA release | 1,747,018 |

The full processing output is provided rather than truncating the release to the
number appearing in the dataset name.

## Data Files

```text
raw/
├── part-00000.parquet  # 1,000 videos
├── part-00001.parquet  # 1,000 videos
├── ...
├── part-00018.parquet  # 1,000 videos
└── part-00019.parquet  #   702 videos
```

Each Parquet row represents one video.

## Schema

```text
data_source: large_string
video_source: large_string
diff_caption: list<large_string>
detailed_caption: list<large_string>
local_caption: list<large_string>
overview_caption: large_string
scene_list: map<string, Scene>
caption: large_string
general_qa: map<string, QA>

Scene: struct<
  scene_hint: string,
  caption: string,
  QA_pair: map<string, QA>
>

QA: struct<
  Dimension: string,
  Question: string,
  Answer: string,
  Options: list<string>,
  Answer_choices: string
>
```

The released column names correspond to the generation records as follows:

| Released column | Generation field |
| --- | --- |
| `diff_caption` | `different` |
| `detailed_caption` | `attention` |
| `local_caption` | `merged` |

The internal `frame_range` field was removed from each `scene_list` entry. Scene IDs,
scene hints, scene captions, and scene-level QA pairs are retained.

## Loading the Dataset

### PyArrow

```python
import pyarrow.parquet as pq

table = pq.read_table(
    "hf://datasets/leaderonehit/GLaVE-1.2M/raw/part-00000.parquet"
)
print(table.schema)
print(table.slice(0, 1).to_pylist()[0])
```

### Download a Snapshot

```python
from huggingface_hub import snapshot_download

dataset_dir = snapshot_download(
    repo_id="leaderonehit/GLaVE-1.2M",
    repo_type="dataset",
    allow_patterns="raw/*.parquet",
)
```

`scene_list`, `QA_pair`, and `general_qa` use the Parquet Map logical type. PyArrow
returns a Map value as a list of `(key, value)` pairs; call `dict(value)` when a
regular Python dictionary is preferred.

## Source Information

The source columns follow the provenance metadata of
[LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K).
Download the original videos from that dataset and use these two columns to locate
the corresponding source file. For example:

```text
data_source:  1_2_m_academic_v0_1
video_source: academic_source/Charades/293L4.mp4
```

## License

All data and code are provided strictly for academic and non-commercial use. Please
contact the authors if further clarification or permissions are needed. Users are
also responsible for complying with the licenses and terms of the corresponding
source videos.

## Citation

```bibtex
@article{xu2025glavecap,
  title   = {GLaVE-Cap: Global-Local Aligned Video Captioning with Vision Expert Integration},
  author  = {Xu, Wan and Zhu, Feng and Zeng, Yihan and Guo, Yuanfan and Liu, Ming and Xu, Hang and Zuo, Wangmeng},
  journal = {arXiv preprint arXiv:2509.11360},
  year    = {2025}
}
```