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Attention mechanisms have been widely applied in the Visual QuestionAnswering (VQA) task, as they help to focus on the area-of-interest of bothvisual and textual information. To answer the questions correctly, the modelneeds to selectively target different areas of an image, which suggests that anattention-based model may benefit from an explicit attention supervision. Inthis work, we aim to address the problem of adding attention supervision to VQAmodels. Since there is a lack of human attention data, we first propose a HumanAttention Network (HAN) to generate human-like attention maps, training on arecently released dataset called Human ATtention Dataset (VQA-HAT). Then, weapply the pre-trained HAN on the VQA v2.0 dataset to automatically produce thehuman-like attention maps for all image-question pairs. The generatedhuman-like attention map dataset for the VQA v2.0 dataset is named asHuman-Like ATtention (HLAT) dataset. Finally, we apply human-like attentionsupervision to an attention-based VQA model. The experiments show that addinghuman-like supervision yields a more accurate attention together with a betterperformance, showing a promising future for human-like attention supervision inVQA.

Exploring Human-like Attention Supervision in Visual Question Answering