  Aided by recent advances in Deep Learning, Image Caption Generation has seen
tremendous progress over the last few years. Most methods use transfer learning
to extract visual information, in the form of image features, with the help of
pre-trained Convolutional Neural Network models followed by transformation of
the visual information using a Caption Generator module to generate the output
sentences. Different methods have used different Convolutional Neural Network
Architectures and, to the best of our knowledge, there is no systematic study
which compares the relative efficacy of different Convolutional Neural Network
architectures for extracting the visual information. In this work, we have
evaluated 17 different Convolutional Neural Networks on two popular Image
Caption Generation frameworks: the first based on Neural Image Caption (NIC)
generation model and the second based on Soft-Attention framework. We observe
that model complexity of Convolutional Neural Network, as measured by number of
parameters, and the accuracy of the model on Object Recognition task does not
necessarily co-relate with its efficacy on feature extraction for Image Caption
Generation task.
