Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Paper
•
2212.00259
•
Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 183, in _generate_tables
df = pandas_read_json(f)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 791, in read_json
json_reader = JsonReader(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 905, in __init__
self.data = self._preprocess_data(data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
data = data.read()
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
out = read(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "<frozen codecs>", line 322, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 544, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
raise e
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
[CVPR 2023 Highlight (top 2.5%)]
Paper: Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Authors: Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, Alan Yuille
Dataset Description
Super-CLEVR is a synthetic dataset designed to systematically study the domain robustness of visual reasoning models across four key factors:
- Visual complexity — varying levels of scene and object complexity
- Question redundancy — controlling redundant information in questions
- Concept distribution — shifts in the distribution of visual concepts
- Concept compositionality — novel compositions of known concepts
Dataset
Super-CLEVR contains 30k images of vehicles (from UDA-Part) randomly placed in the scenes, with 10 question-answer pairs for each image. The vehicles have part annotations and so the objects in the images can have distinct part attributes.
Here [link] is the list of objects and parts in Super-CLEVR scenes.
The first 20k images and paired are used for training, the next 5k for validation and the last 5k for testing.
The dataset is available on Hugging Face:
| Data | Download Link |
|---|---|
| images | images.zip |
| scenes | superCLEVR_scenes.json |
| questions | superCLEVR_questions_30k.json |
| questions (- redundancy) | superCLEVR_questions_30k_NoRedundant.json |
| questions (+ redundancy) | superCLEVR_questions_30k_AllRedundant.json |
Usage
from huggingface_hub import hf_hub_download
# Download a specific file
path = hf_hub_download(
repo_id="RyanWW/Super-CLEVR",
filename="superCLEVR_questions_30k.json",
repo_type="dataset",
)
Citation
@inproceedings{li2023super,
title={Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning},
author={Li, Zhuowan and Wang, Xingrui and Stengel-Eskin, Elias and Kortylewski, Adam and Ma, Wufei and Van Durme, Benjamin and Yuille, Alan L},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={14963--14973},
year={2023}
}
Links
- Code: github.com/Lizw14/Super-CLEVR
- Paper: arxiv.org/abs/2212.00259
License
This dataset is released under the MIT License.
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