Show Me a Story: Towards Coherent Neural Story Illustration
| Autor: | Carlos Manuel Muñiz, Leonid Sigal, Dimitris N. Metaxas, Lezi Wang, Hareesh Ravi, Mubbasir Kapadia |
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| Rok vydání: | 2018 |
| Předmět: |
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Artificial neural network Computer science business.industry 02 engineering and technology Coherence (statistics) Resolution (logic) computer.software_genre Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence Representation (mathematics) business computer Natural language processing Storytelling |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2018.00794 |
| Popis: | We propose an end-to-end network for visual illustration of a sequence of sentences forming a story. At the core of our model is the ability to model the inter-related nature of the sentences within a story, as well as the ability to learn coherence to support reference resolution. The framework takes the form of an encoder-decoder architecture, where sentences are encoded using a hierarchical two-level sentence-story GRU, combined with an encoding of coherence, and sequentially decoded using a predicted feature representation into a consistent illustrative image sequence. We optimize all parameters of our network in an end-to-end fashion with respect to order embedding loss, encoding entailment between images and sentences. Experiments on the VIST storytelling dataset [9] highlight the importance of our algorithmic choices and efficacy of our overall model. |
| Databáze: | OpenAIRE |
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