Image Caption Generation and Comprehensive Comparison of Image Encoders

Image caption generation is a stimulating multimodal task. Substantial advancements have been made in the field of deep learning notably in computer vision and natural language processing. Yet, human-generated captions are still considered better, which makes it a challenging application for interactive machine learning. In this paper, we aim to compare different transfer learning techniques and develop a novel architecture to improve image captioning accuracy. We compute image feature vectors using different state-of-the-art transfer learning models which are fed into an Encoder-Decoder network based on Stacked LSTMs with soft attention, along with embedded text to generate high accuracy captions. We have compared these models on several benchmark datasets based on different evaluation metrics like BLEU and METEOR.