---
license: other
license_name: egotaskqa-research-only
license_link: https://sites.google.com/view/egotaskqa
task_categories:
- visual-question-answering
- multiple-choice
language:
- en
size_categories:
- 10K<n<100K
pretty_name: EgoTaskQA-MCQ
configs:
- config_name: default
  data_files:
  - split: test
    path: test.json
---

# EgoTaskQA-MCQ

Multiple-choice variant of the [EgoTaskQA](https://sites.google.com/view/egotaskqa) benchmark
(Jia et al., NeurIPS 2022). Each item pairs an egocentric video clip with a 5-way MCQ
(A–E) over object states, actions, and goals.

## Format

`test.json` is a list of records:

```json
{
  "video_path": "27k-22-11-2|P1|4546|4986.mp4",
  "q": "What is the status of sandwich before ...",
  "option": {"A": "on top of plate", "B": "...", "C": "...", "D": "...", "E": "..."},
  "a": "A"
}
```

`a` is the letter of the correct option.

## Videos (not included)

This repository hosts annotations only — the 2,337 egocentric video clips
referenced by `video_path` are **not redistributed** here, since the original
EgoTaskQA dataset is gated under a research-only license.

To obtain the videos:

1. Visit https://sites.google.com/view/egotaskqa and submit the license-agreement
   form linked from the Download page.
2. Once granted access, download the `qa_videos/` directory.
3. Either place the files under `~/.cache/lmms_eval/egotaskqa/videos/`, or point
   the `EGOTASKQA_VIDEO_DIR` environment variable at the directory.

Filenames in `qa_videos/` match the `video_path` field in this dataset
(`<scene>-<...>|<participant>|<start>|<end>.mp4`); no renaming is required.

## License

The MCQ annotations in this repo are released for research use only.
Upstream video content remains subject to the EgoTaskQA license; users must
obtain it through the official channel above.

## Citation

```
@inproceedings{jia2022egotaskqa,
  title={EgoTaskQA: Understanding Human Tasks in Egocentric Videos},
  author={Jia, Baoxiong and Lei, Ting and Zhu, Song-Chun and Huang, Siyuan},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2022}
}
```