Innovations:

• The authors develop a convolutional ( CNN-based ) image captioning method that shows comparable performance to an LSTM based method on standard metrics.
• The authors analyze the characteristics of CNN and LSTM nets and provide useful insights such as -- CNNs produce more entropy ( useful for diverse predictions ), better classification accuracy, and do not suffer from vanishing gradients.

Improvements:

• A Convolutional Neural Network with Attention mechanism.

General Points:

• Image Captioning is applicable to virtual assistants, editing tools, image indexing and support of the disabled.
• Image Captioning is a basic ingredient for more complex operations such as storytelling and visual summarization.

Paper Reading - Convolutional Image Captioning ( CVPR 2018 )