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Since its appearance, Visual Question Answering (VQA, i.e. answering aquestion posed over an image), has always been treated as a classificationproblem over a set of predefined answers. Despite its convenience, thisclassification approach poorly reflects the semantics of the problem limitingthe answering to a choice between independent proposals, without taking intoaccount the similarity between them (e.g. equally penalizing for answering cator German shepherd instead of dog). We address this issue by proposing (1) twomeasures of proximity between VQA classes, and (2) a corresponding loss whichtakes into account the estimated proximity. This significantly improves thegeneralization of VQA models by reducing their language bias. In particular, weshow that our approach is completely model-agnostic since it allows consistentimprovements with three different VQA models. Finally, by combining our methodwith a language bias reduction approach, we report SOTA-level performance onthe challenging VQAv2-CP dataset.

Estimating semantic structure for the VQA answer space