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- W2949362007 abstract "Visual attention has shown usefulness in image captioning, with the goal of enabling a caption model to selectively focus on regions of interest. Existing models typically rely on top-down language information and learn attention implicitly by optimizing the captioning objectives. While somewhat effective, the learned top-down attention can fail to focus on correct regions of interest without direct supervision of attention. Inspired by the human visual system which is driven by not only the task-specific top-down signals but also the visual stimuli, we in this work propose to use both types of attention for image captioning. In particular, we highlight the complementary nature of the two types of attention and develop a model (Boosted Attention) to integrate them for image captioning. We validate the proposed approach with state-of-the-art performance across various evaluation metrics." @default.
- W2949362007 created "2019-06-27" @default.
- W2949362007 creator A5047419128 @default.
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- W2949362007 date "2019-03-18" @default.
- W2949362007 modified "2023-09-26" @default.
- W2949362007 title "Boosted Attention: Leveraging Human Attention for Image Captioning" @default.
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- W2949362007 doi "https://doi.org/10.48550/arxiv.1904.00767" @default.
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