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Visual Question Answering (VQA) models are prone to learn the shortcutsolution formed by dataset biases rather than the intended solution. Toevaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CPv2 dataset introduces a distribution shift between the training and test setgiven a question type. In this way, the model cannot use the training setshortcut (from question type to answer) to perform well on the test set.However, VQA-CP v2 only considers one type of shortcut and thus still cannotguarantee that the model relies on the intended solution rather than a solutionspecific to this shortcut. To overcome this limitation, we propose a newdataset that considers varying types of shortcuts by constructing differentdistribution shifts in multiple OOD test sets. In addition, we overcome thethree troubling practices in the use of VQA-CP v2, e.g., selecting models usingOOD test sets, and further standardize OOD evaluation procedure. Our benchmarkprovides a more rigorous and comprehensive testbed for shortcut learning inVQA. We benchmark recent methods and find that methods specifically designedfor particular shortcuts fail to simultaneously generalize to our varying OODtest sets. We also systematically study the varying shortcuts and provideseveral valuable findings, which may promote the exploration of shortcutlearning in VQA.

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA