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• W3034733309 abstract "Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences. We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics. The code of our paper has been made publicly available." @default.
• W3034733309 created "2020-06-19" @default.
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• W3034733309 date "2020-01-01" @default.
• W3034733309 modified "2023-10-14" @default.
• W3034733309 title "Improving Image Captioning with Better Use of Caption" @default.
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• W3034733309 doi "https://doi.org/10.18653/v1/2020.acl-main.664" @default.
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