A sequential guiding network with attention for image captioning
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The recent advances of deep learning in both computer vision (CV)and natural language processing (NLP) provide us a new way of un-derstanding semantics, by which we can deal with more challeng-ing tasks such as automatic description generation from natural im-ages. In this challenge, the encoder-decoder framework has achievedpromising performance when a convolutional neural network (CNN)is used as image encoder and a recurrent neural network (RNN) asdecoder. In this paper, we introduce a sequential guiding networkthat guides the decoder during word generation. The new model is anextension of the encoder-decoder framework with attention that hasan additional guiding long short-term memory (LSTM) and can betrained in an end-to-end manner by using image/descriptions pairs.We validate our approach by conducting extensive experiments on abenchmark dataset, i.e., MS COCO Captions. The proposed model achieves significant improvement comparing to the other state-of-the-art deep learning models.
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