A modularized architecture of multi-branch convolutional neural network for image captioning

Abstract

Image captioning is a comprehensive task in computer vision (CV) and natural language processing (NLP). It can complete conversion from image to text, that is, the algorithm automatically generates corresponding descriptive text according to the input image. In this paper, we present an end-to-end model that takes deep convolutional neural network (CNN) as the encoder and recurrent neural network (RNN) as the decoder. In order to get better image captioning extraction, we propose a highly modularized multi-branch CNN, which could increase accuracy while maintaining the number of hyper-parameters unchanged. This strategy provides a simply designed network consists of parallel sub-modules of the same structure. While traditional CNN goes deeper and wider to increase accuracy, our proposed method is more effective with a simple design, which is easier to optimize for practical application. Experiments are conducted on Flickr8k, Flickr30k and MSCOCO entities. Results demonstrate that our method achieves state of the art performances in terms of caption quality.