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metadata
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: random_video_3d_what_move
      num_bytes: 8721672
      num_examples: 300
    - name: random_video_3d_where_move
      num_bytes: 9374584
      num_examples: 300
    - name: random_video_3d_what_attribute_move
      num_bytes: 8721672
      num_examples: 300
    - name: random_video_3d_what_rotate
      num_bytes: 12685235
      num_examples: 300
    - name: random_video_3d_where_rotate
      num_bytes: 12254138
      num_examples: 300
    - name: random_video_3d_what_attribute_rotate
      num_bytes: 12462182
      num_examples: 300
    - name: random_video_sg_what_object
      num_bytes: 525095218
      num_examples: 300
    - name: random_video_sg_what_relation
      num_bytes: 534471346
      num_examples: 300
    - name: random_video_sg_what_action
      num_bytes: 571747390
      num_examples: 300
  download_size: 612311506
  dataset_size: 1695533437
configs:
  - config_name: default
    data_files:
      - split: random_video_3d_what_move
        path: data/random_video_3d_what_move-*
      - split: random_video_3d_where_move
        path: data/random_video_3d_where_move-*
      - split: random_video_3d_what_attribute_move
        path: data/random_video_3d_what_attribute_move-*
      - split: random_video_3d_what_rotate
        path: data/random_video_3d_what_rotate-*
      - split: random_video_3d_where_rotate
        path: data/random_video_3d_where_rotate-*
      - split: random_video_3d_what_attribute_rotate
        path: data/random_video_3d_what_attribute_rotate-*
      - split: random_video_sg_what_object
        path: data/random_video_sg_what_object-*
      - split: random_video_sg_what_relation
        path: data/random_video_sg_what_relation-*
      - split: random_video_sg_what_action
        path: data/random_video_sg_what_action-*

Dataset Card for TaskMeAnything-v1-videoqa-random

TaskMeAnything-v1-videoqa-random dataset

🌐 Website | πŸ“‘ Paper | πŸ€— Huggingface | πŸ’» Interface

If you like our project, please give us a star ⭐ on GitHub for latest update.

TaskMeAnything-v1-Random

TaskMeAnything-v1-videoqa-random is a dataset which randomly sampled questions from TaskMeAnything-v1, including 2,700 VideoQA questions. The dataset contains 9 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-Random VideoQA Dataset and Convert Video Binary Stream to mp4

  • Since Huggingface does not support saving .mp4 files in datasets, we save videos in the format of binary streams. After loading, you can convert the video binary stream to .mp4 using the following method.
import datasets

dataset_name = 'weikaih/TaskMeAnything-v1-videoqa-random'
dataset = datasets.load_dataset(dataset_name, split = TASK_GENERATOR_SPLIT)

# example: convert binary stream in dataset to .mp4 files
video_binary = dataset[0]['video']
with open('/path/save/video.mp4', 'wb') as f:
    f.write(video_binary)

where TASK_GENERATOR_SPLIT is one of the task generators, eg, random_video_3d_what_move.

Evaluation Results

Overall

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Breakdown performance on each task types

image/png

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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

Citation

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}
}