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The current success of modern visual reasoning systems is arguably attributedto cross-modality attention mechanisms. However, in deliberative reasoning suchas in VQA, attention is unconstrained at each step, and thus may serve as astatistical pooling mechanism rather than a semantic operation intended toselect information relevant to inference. This is because at training time,attention is only guided by a very sparse signal (i.e. the answer label) at theend of the inference chain. This causes the cross-modality attention weights todeviate from the desired visual-language bindings. To rectify this deviation,we propose to guide the attention mechanism using explicit linguistic-visualgrounding. This grounding is derived by connecting structured linguisticconcepts in the query to their referents among the visual objects. Here welearn the grounding from the pairing of questions and images alone, without theneed for answer annotation or external grounding supervision. This groundingguides the attention mechanism inside VQA models through a duality ofmechanisms: pre-training attention weight calculation and directly guiding theweights at inference time on a case-by-case basis. The resultant algorithm iscapable of probing attention-based reasoning models, injecting relevantassociative knowledge, and regulating the core reasoning process. This scalableenhancement improves the performance of VQA models, fortifies their robustnessto limited access to supervised data, and increases interpretability.

Guiding Visual Question Answering with Attention Priors