Design and development of context aware deep learning models for image captioning

Abstract

Image captioning is a brand-new study area in the science of computer vision. The primary goal of picture captioning is to create a natural language description for the input image. In recent years, research on natural language processing and computer vision has become increasingly interested in the problem of automatically synthesising descriptive phrases for photos. Image captioning is a crucial task that demands both the ability to create precise and accurate description phrases as well as a semantic understanding of the images. The objective of image captioning is to develop a natural language representation of an image. It is a difficult task to define the content of an image. To facilitate elaborate definition, the detection and recognition of objects, people, correlations and corresponding features is required. In order to execute Image Captioning methods, these present computer vision tasks must not only collect information contained in images but also extract semantic associations of acquired visual information corresponding to verbal expressions. Open domain captioning is a big challenge, due to its requirement for a deep knowledge on the global and the local entities present in an image, in addition to their features and associations. The template-based techniques, at first maps every sentence pieces (e.g., subject, verb, object) with the words identified from image content and then the sentence is generated with predefined language samples. Apparently, a majority of them highly rely on the samples of sentence and always develop sentences that are syntactically structured newline