|
--- |
|
language: |
|
- en |
|
dataset_info: |
|
features: |
|
- name: id |
|
dtype: string |
|
- name: question |
|
dtype: string |
|
- name: options |
|
list: string |
|
- name: answer |
|
dtype: string |
|
- name: task_plan |
|
dtype: string |
|
- name: video |
|
dtype: binary |
|
splits: |
|
- name: video_3d_what_move |
|
num_bytes: 13156784 |
|
num_examples: 408 |
|
- name: video_3d_where_move |
|
num_bytes: 11911124 |
|
num_examples: 402 |
|
- name: video_3d_what_attribute_move |
|
num_bytes: 11401767 |
|
num_examples: 390 |
|
- name: video_3d_what_rotate |
|
num_bytes: 17223350 |
|
num_examples: 411 |
|
- name: video_3d_where_rotate |
|
num_bytes: 14860244 |
|
num_examples: 378 |
|
- name: video_3d_what_attribute_rotate |
|
num_bytes: 15306580 |
|
num_examples: 405 |
|
- name: video_sg_what_object |
|
num_bytes: 654650009 |
|
num_examples: 387 |
|
- name: video_sg_what_relation |
|
num_bytes: 707499456 |
|
num_examples: 411 |
|
- name: video_sg_what_action |
|
num_bytes: 636426745 |
|
num_examples: 375 |
|
download_size: 748458643 |
|
dataset_size: 2082436059 |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: video_3d_what_move |
|
path: data/video_3d_what_move-* |
|
- split: video_3d_where_move |
|
path: data/video_3d_where_move-* |
|
- split: video_3d_what_attribute_move |
|
path: data/video_3d_what_attribute_move-* |
|
- split: video_3d_what_rotate |
|
path: data/video_3d_what_rotate-* |
|
- split: video_3d_where_rotate |
|
path: data/video_3d_where_rotate-* |
|
- split: video_3d_what_attribute_rotate |
|
path: data/video_3d_what_attribute_rotate-* |
|
- split: video_sg_what_object |
|
path: data/video_sg_what_object-* |
|
- split: video_sg_what_relation |
|
path: data/video_sg_what_relation-* |
|
- split: video_sg_what_action |
|
path: data/video_sg_what_action-* |
|
--- |
|
# Dataset Card for TaskMeAnything-v1-videoqa-2024 |
|
<h2 align="center"> TaskMeAnything-v1-videoqa-2024 benchmark dataset</h2> |
|
|
|
<h2 align="center"> <a href="https://www.task-me-anything.org/">🌐 Website</a> | <a href="https://arxiv.org/abs/2406.11775">📑 Paper</a> | <a href="https://huggingface.co/collections/jieyuz2/taskmeanything-664ebf028ab2524c0380526a">🤗 Huggingface</a> | <a href="https://huggingface.co/spaces/zixianma/TaskMeAnything-UI">💻 Interface</a></h2> |
|
|
|
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> |
|
|
|
## TaskMeAnything-v1-2024-Videoqa |
|
[TaskMeAnything-v1-videoqa-2024](https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-videoqa-2024) is a benchmark for reflecting the current progress of MLMs by `automatically` finding tasks that SOTA MLMs struggle with using the TaskMeAnything Top-K queries. |
|
This benchmark includes 2,394 3d video questions and 1,173 real video questions that the TaskMeAnything algorithm automatically approximated as challenging for over 12 popular MLMs. |
|
|
|
|
|
The dataset contains 19 splits, while each splits contains 300+ questions from a specific task generator in TaskMeAnything-v1. For each row of dataset, it includes: video, question, options, answer and its corresponding task plan. |
|
|
|
## Load TaskMeAnything-v1-2024 VideoQA Dataset |
|
``` |
|
import datasets |
|
|
|
dataset_name = 'weikaih/TaskMeAnything-v1-videoqa-2024' |
|
dataset = datasets.load_dataset(dataset_name, split = TASK_GENERATOR_SPLIT) |
|
``` |
|
where `TASK_GENERATOR_SPLIT` is one of the task generators, eg, `2024_2d_how_many`. |
|
|
|
## Evaluation Results |
|
|
|
### Overall |
|
|
|
|
|
 |
|
|
|
### Breakdown performance on each task types |
|
|
|
 |
|
|
|
 |
|
|
|
## Out-of-Scope Use |
|
This dataset should not be used for training models. |
|
|
|
|
|
## Disclaimers |
|
**TaskMeAnything** and its associated resources are provided for research and educational purposes only. |
|
The authors and contributors make no warranties regarding the accuracy or reliability of the data and software. |
|
Users are responsible for ensuring their use complies with applicable laws and regulations. |
|
The project is not liable for any damages or losses resulting from the use of these resources. |
|
|
|
## Contact |
|
|
|
- Jieyu Zhang: jieyuz2@cs.washington.edu |
|
|
|
## Citation |
|
**BibTeX:** |
|
```bibtex |
|
@article{zhang2024task, |
|
title={Task Me Anything}, |
|
author={Zhang, Jieyu and Huang, Weikai and Ma, Zixian and Michel, Oscar and He, Dong and Gupta, Tanmay and Ma, Wei-Chiu and Farhadi, Ali and Kembhavi, Aniruddha and Krishna, Ranjay}, |
|
journal={arXiv preprint arXiv:2406.11775}, |
|
year={2024} |
|
} |
|
``` |
