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Hierarchically-Attentive RNN for Album Summarization and Storytelling
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We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.
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Cited by 1 Pith paper
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From Image Captioning to Visual Storytelling
Visual storytelling improves by treating it as image captioning followed by language-to-language story generation, with a new 'ideality' metric to gauge distance from an oracle.
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