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In this paper, we present a multimodal Recurrent Neural Network (m-RNN) modelfor generating novel image captions. It directly models the probabilitydistribution of generating a word given previous words and an image. Imagecaptions are generated by sampling from this distribution. The model consistsof two sub-networks: a deep recurrent neural network for sentences and a deepconvolutional network for images. These two sub-networks interact with eachother in a multimodal layer to form the whole m-RNN model. The effectiveness ofour 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. Inaddition, we apply the m-RNN model to retrieval tasks for retrieving images orsentences, and achieves significant performance improvement over thestate-of-the-art methods which directly optimize the ranking objective functionfor retrieval. The project page of this work is:www.stat.ucla.edu/~junhua.mao/m-RNN.html .

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)