Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022)

Abstract: Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models’ reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (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 cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.

## introduction visual question answering (vqa) @xcite is a multi-modal task, involving the comprehension and reasoning on vision and language. despite the remarkable performance on many vqa datasets such as vqa v2 @xcite , vqa models have been criticized for their tendency to depend on the biases in training set. that is, they tend to directly output the answer according to question type (shortcut solutions) without actual reasoning @xcite (intended solution) @xcite @xcite . this widely studied language priors problem is a typical symptom of shortcut learning @xcite ) (app. a.1). in spite of such defect, the models can still reach artificially excellent performance on vqa v2 whose test distribution is the same as the training distribution, i.e., under the in-distribution (iid) setting. to address this problem, vqa-cp v2 @xcite was constructed by re-organizing vqa v2 dataset such that answer distributions of the same question type are different between the training set and the test set, i.e., under the ood setting. vqa-cp v2 can make the shortcut (from the question type to the answer) in the training set invalid in the test set and it has become a widely-used ood benchmark in vqa community. with models' performance on vqa-cp v2 continually improving, it seems that existing methods @xcite @xcite have been able to overcome the shortcut learning problem. however, through analyzing vqa-cp v2 and existing methods, we point out that there are two aspects needed to be improved: first, vqa-cp v2 introduces only one specific type of controlled distribution shift, and thus its ood setting can only evaluate the model's reasoning ability beyond one specific shortcut rather than the intended solution. as shown in fig. 1(a) , despite performing well on vqa-cp v2, the debiasing method lmh @xcite , can only boost its backbone model updn on few certain ood test sets while fails to generalize to other ood sets. this shows vqa-cp v2 cannot identify whether the models rely on other types of shortcuts (e.g., correlations between visual objects and answers). therefore, as shown in fig. 1(b) , more ood test sets are needed to measure the reliance of the model on different types of shortcuts. as the performance on more ood test sets is improved simultaneously, the more confidently can the model be deemed to have learned the intended solution. moreover, some studies @xcite demonstrate that abundant shortcuts in data are derived from the real-world stereotypes, e.g., the sky is blue. thus, to establish a reliable diagnostic for vqa models, constructing a new benchmark with multiple ood test sets corresponding to varying types of shortcuts is an urgent need. second, three troubling practices @xcite exist in the experimental setup and the design of existing methods: 1) the training and the test distribution of vqa-cp v2 are almost inverse against each other, and this known construction of the ood setting can be easily exploited to achieve good performance; 2) using the ood test set for model selection; 3) iid performance is evaluated after retraining. these practices do not conform with the real-world ood scenarios, and thus making the evaluated ood performance unreliable. to alleviate the single-shortcut limitation and overcome three above-mentioned issues, we construct and publicly release a new vqa benchmark considering varying shortcuts (sec. 3.2), named vqa-vs, and further standardize the ood testing procedure (sec. 2.2). in particular, we select varying shortcuts including language-based, visual-based and multi-modality ones, which aims at covering different types of superficial correlations. for each selected shortcut, we propose a method based on mutual information to select the shortcut-specific concepts. then we utilize the concepts to group samples, and further introduce nine distribution shifts, based on a shannon entropy method, to construct nine ood test sets according to varying shortcuts. besides, our benchmark also presents an iid validation set and an iid test set for evaluating the iid performance. we benchmark a series of state-of-the-art models on vqa-vs, and find that it may provide more reliable evaluation of the reasoning ability compared to existing benchmarks. moreover, adequate experiments are conducted to present the first systematic study on multiple shortcuts, which may promote the development of shortcut learning in vqa. we can access to a training data from a distribution p ((v, q), a) and vqa task is to minimize the empirical risk: the existing ood benchmarks foot_0 for vqa, e.g., vqa-cp v2, evaluate the robustness of models by the small expected risk for a single different distribution p ′ ((v, q), a): the novel test distribution p ′ can be restricted to the result of a collection of distribution shifts, which are introduced by the interventions on one or several causal variables in the causal graph g of vqa. we denote by p g all the possible interventional distributions over the whole causal graph g, including the unknown and observed causal variables. to stay robust against distribution shifts on possible causal variables, we focus on the overall ood risk (instead of the worst case ood risk): ## conclusion to solve the single-shortcut problem and three issues in the use of current ood benchmark vqa-cp v2, we construct and publish a new ood benchmark vqa-vs including nine ood test sets corresponding to varying shortcuts. compared with vqa-cp v2, vqa-vs can provide a more reliable and comprehensive testbed for the reasoning ability of debiasing methods. based on vqa-vs, we benchmark the recent vqa models to conduct a systematical study on the various shortcuts.