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• W2998988444 abstract "Generating natural and accurate descriptions in image captioning has always been a challenge. In this paper, we propose a novel recall mechanism to imitate the way human conduct captioning. There are three parts in our recall mechanism : recall unit, semantic guide (SG) and recalled-word slot (RWS). Recall unit is a text-retrieval module designed to retrieve recalled words for images. SG and RWS are designed for the best use of recalled words. SG branch can generate a recalled context, which can guide the process of generating caption. RWS branch is responsible for copying recalled words to the caption. Inspired by pointing mechanism in text summarization, we adopt a soft switch to balance the generated-word probabilities between SG and RWS. In the CIDEr optimization step, we also introduce an individual recalled-word reward (WR) to boost training. Our proposed methods (SG+RWS+WR) achieve BLEU-4 / CIDEr / SPICE scores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.7 / 129.1 / 22.4 with CIDEr optimization on MSCOCO Karpathy test split, which surpass the results of other state-of-the-art methods." @default.
• W2998988444 created "2020-01-23" @default.
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• W2998988444 date "2020-04-03" @default.
• W2998988444 modified "2023-09-22" @default.
• W2998988444 title "Show, Recall, and Tell: Image Captioning with Recall Mechanism" @default.
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• W2998988444 doi "https://doi.org/10.1609/aaai.v34i07.6898" @default.
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