Enhancing Efficiency and Quality of Image Caption Generation with CARU

Image caption is textual explanation automatically generated by a computer according to the content in an image. It involves both image and natural language processing, and thus becomes an important research topic in pattern recognition. Deep learning has been successful in accomplishing this task, and the quality of captions generated by existing methods is already high. However, due to the broadness and variety of image caption applications, the current generated captions are still not sufficiently detailed, and the training efficiency can also be improved. Therefore, under the encoder-decoder framework of deep learning, how to use fewer parameters to improve the training efficiency and retain the quality of the generated image descriptions is a huge challenge. In this work, we introduce an improved method based on the encoder-decoder structure, adding an attention mechanism, and applying the content adaptive recurrent unit (CARU), as the decoder, to generate image captions. Inspired by GRU, CARU is designed to have comparable performance with fewer parameters, and is sensitive to the features in hidden layers. The experimental results show, based on MsCOCO dataset, the proposed method achieved better performance than that using GRU as the decoder, and took less training time, effectively improves the training efficiency.