NDLI: Cascade recurrent neural network for image caption generation

Content Provider	IET Digital Library
Author	Wu, Jie Hu, Haifeng
Abstract	A new cascade recurrent neural network (CRNN) for image caption generation is proposed. Different from the classical multimodal recurrent neural network, which only uses a single network for extracting unidirectional syntactic features, CRNN adopts a cascade network for learning visual-language interactions from forward and backward directions, which can exploit the deep semantic contexts contained in the image. In the proposed framework, two embedding layers for dense word expression are constructed. A new stacked Gated Recurrent Unit is designed for learning image-word mappings. The effectiveness of the CRNN model is verified with adopting the commonly used MSCOCO datasets, where the results indicate CRNN can achieve better performance compared with the state-of-the-art image captioning methods such as Google NIC, multimodal recurrent neural network and so on.
Starting Page	1642
Ending Page	1643
Page Count	2
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 25, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/25
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2017.3159
Journal	Electronics Letters
Publisher Date	2017-11-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cascade Network Cascade Recurrent Neural Network Classical Multimodal Recurrent Neural Network Computer Vision And Image Processing Technique CRNN Model Deep Semantic Contexts Dense Word Expression Embedding Layer Feature Extraction Google NIC Image Caption Generation Image Captioning Method Image Recognition Image-word Mappings MSCOCO Datasets Neural Computing Technique Recurrent Neural Nets Stacked Gated Recurrent Unit Unidirectional Syntactic Feature Visual-language Interaction
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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