A Comparative Study on Deep CNN Visual Encoders for Image Captioning

Captioning an image is the process of describing it with syntactically and semantically meaningful terms. An image caption generator is developed by the integration of computer vision and natural language processing technology. Despite the fact that numerous techniques for generating image captions have been developed, the result is inadequate and the need for research in this area is still a demanding topic. The human process of describing any image is by seeing, focusing and captioning, which is equivalent that of feature representation, visual encoding and language generation for the image captioning systems. This study presents the construction of a simple deep learning-based image captioning model and investigates the efficacy of different visual encoding methods employed in the model. We have analyzed and compared the performance of six different pre-trained CNN visual encoding models using Bilingual Evaluation Understudy (BLEU) scores.