Document (#38559)

Author
Mao, J.
Xu, W.
Yang, Y.
Wang, J.
Yuille, A.L.
Title
Explain images with multimodal recurrent neural networks
Year
2014
Abstract
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images. It directly models the probability distribution of generating a word given previous words and the image. Image descriptions 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 three benchmark datasets (IAPR TC-12 [8], Flickr 8K [28], and Flickr 30K [13]). Our model outperforms the state-of-the-art generative method. In addition, the m-RNN model can be applied 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.
Theme
Automatisches Indexieren
Form
Bilder
Object
Baidu

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  5. Karpathy, A.: ¬The unreasonable effectiveness of recurrent neural networks (2015) 0.31
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