  Despite advances in Visual Question Answering (VQA), the ability of models to
assess their own correctness remains underexplored. Recent work has shown that
VQA models, out-of-the-box, can have difficulties abstaining from answering
when they are wrong. The option to abstain, also called Selective Prediction,
is highly relevant when deploying systems to users who must trust the system's
output (e.g., VQA assistants for users with visual impairments). For such
scenarios, abstention can be especially important as users may provide
out-of-distribution (OOD) or adversarial inputs that make incorrect answers
more likely. In this work, we explore Selective VQA in both in-distribution
(ID) and OOD scenarios, where models are presented with mixtures of ID and OOD
data. The goal is to maximize the number of questions answered while minimizing
the risk of error on those questions. We propose a simple yet effective
Learning from Your Peers (LYP) approach for training multimodal selection
functions for making abstention decisions. Our approach uses predictions from
models trained on distinct subsets of the training data as targets for
optimizing a Selective VQA model. It does not require additional manual labels
or held-out data and provides a signal for identifying examples that are
easy/difficult to generalize to. In our extensive evaluations, we show this
benefits a number of models across different architectures and scales. Overall,
for ID, we reach 32.92% in the selective prediction metric coverage at 1% risk
of error (C@1%) which doubles the previous best coverage of 15.79% on this
task. For mixed ID/OOD, using models' softmax confidences for abstention
decisions performs very poorly, answering <5% of questions at 1% risk of error
even when faced with only 10% OOD examples, but a learned selection function
with LYP can increase that to 25.38% C@1%.
