license: apache-2.0
dataset_info:
- config_name: CLEVRER
features:
- name: video_filename
dtype: string
- name: scene_index
dtype: int64
- name: question_text
dtype: string
- name: answer_text
dtype: string
- name: attributes_list
sequence: string
splits:
- name: train
num_bytes: 2029869
num_examples: 13374
download_size: 203081
dataset_size: 2029869
- config_name: VG_v1
features:
- name: img_id
dtype: int64
- name: orig_qa
dtype: string
- name: question_text
dtype: string
- name: answer_text
dtype: string
splits:
- name: train
num_bytes: 26281742
num_examples: 424507
download_size: 7732035
dataset_size: 26281742
- config_name: vg_V1
features:
- name: img_id
dtype: int64
- name: orig_qa
dtype: string
- name: question_text
dtype: string
- name: answer_text
dtype: string
splits:
- name: train
num_bytes: 26281742
num_examples: 424507
download_size: 7732035
dataset_size: 26281742
configs:
- config_name: CLEVRER
data_files:
- split: train
path: CLEVRER/train-*
- config_name: VG_v1
data_files:
- split: train
path: VG_v1/train-*
- config_name: vg_V1
data_files:
- split: train
path: vg_V1/train-*
Here we create two datasets (from existing datasets: CLEVRER, VisualGenome) for the Object Counting instruction tuning task.
CLEVRER, a video dataset
CLEVRER has QA pairs for each 5000 training videos.
{'video_filename': int, 'scene_index': str (same as filename), 'questions': list [{'question_type': , 'question_subtype': , 'question_text': , 'answer_text': , 'program'(question attributes): }]}
We select 'descriptive' type, 'count' subtype questions, they are object counting task questions. In the 'program' list, it shows how complex the question is (longer means more complex), so we filter out those longer than 9 to reduce difficulty.
CLEVRER contains both positive questions and negative (asking for non-exist objects) questions, so we skip generating negative samples for CLEVRER.
Some questions are 'event' specific, counting moving/stationary objects when a certain event happens. i.e., 'How many objects are stationary when the yellow object enters the scene?'
Downloading videos from: http://clevrer.csail.mit.edu/
VisualGenome, an image dataset
We generate some negative questions for non-exist objects in the image. We use the version 1 image sets. Download from: https://homes.cs.washington.edu/~ranjay/visualgenome/api.html
VisualGenome has 100K+ images. And for the objects in the image, there are attributes associated with each object, we only focus on the color attributes.
For each image, we choose to add (1) 3 non-exist objects and (2) 1 non-exist attribute for existing objects as negative samples.
In the original qa dataset, VG has Object Counting questions, we also include them here, with the 'orig_qa'=='Yes'. For those negative questions we generated, 'orig_qa' =='No'.
{'img_id': str, 'orig_qa': Yes/No, 'question_text': 'How many <attribute> <object in plural form> are there? ', 'answer_text': Numbers.(if exist) or None.(if non-exist) }
For more details, plz refer to the dataset.