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• W1811254738 abstract "In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: this http URL ." @default.
• W1811254738 created "2016-06-24" @default.
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• W1811254738 date "2014-12-20" @default.
• W1811254738 modified "2023-10-06" @default.
• W1811254738 title "Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)" @default.
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