ACRE (Abstract Causal REasoning)
Introduced by Zhang et al. in ACRE: Abstract Causal REasoning Beyond Covariation
Abstract Causal REasoning (ACRE) is a dataset for the systematic evaluation of current vision systems in causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables.
Each split of the dataset is structured as follows:
config/
train.json
val.json
test.json
images/
ACRE_train_00*.png
ACRE_val_00*.png
ACRE_test_00*.png
scenes/
ACRE_train_00*.json
ACRE_val_00*.json
ACRE_test_00*.json
Each image file in the images folder has a corresponding scene description file in scenes with the same name (except for the extension).
Each ACRE problem is named after ACRE_{train/val/test}_{6_digit_problem_idx}_{2_digit_panel_idx}
Papers
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