  Recently Convolutional Neural Networks have been proposed for Sequence
Modelling tasks such as Image Caption Generation. However, unlike Recurrent
Neural Networks, the performance of Convolutional Neural Networks as Decoders
for Image Caption Generation has not been extensively studied. In this work, we
analyse various aspects of Convolutional Neural Network based Decoders such as
Network complexity and depth, use of Data Augmentation, Attention mechanism,
length of sentences used during training, etc on performance of the model. We
perform experiments using Flickr8k and Flickr30k image captioning datasets and
observe that unlike Recurrent Neural Network based Decoder, Convolutional
Decoder for Image Captioning does not generally benefit from increase in
network depth, in the form of stacked Convolutional Layers, and also the use of
Data Augmentation techniques. In addition, use of Attention mechanism also
provides limited performance gains with Convolutional Decoder. Furthermore, we
observe that Convolutional Decoders show performance comparable with Recurrent
Decoders only when trained using sentences of smaller length which contain up
to 15 words but they have limitations when trained using higher sentence
lengths which suggests that Convolutional Decoders may not be able to model
long-term dependencies efficiently. In addition, the Convolutional Decoder
usually performs poorly on CIDEr evaluation metric as compared to Recurrent
Decoder.
