Link of the Paper: https://arxiv.org/abs/1711.09151

Motivation:

• LSTM units are complex and inherently sequential across time.
• Convolutional networks have shown advantages on machine translation and conditional image generation.

Innovation:

• 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.

Improvement:

• Improved performance with a CNN model that uses Attention Mechanism to leverage spatial image features.

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.
• An illustration of a classical RNN architecture for image captioning is provided below.