A Comprehensive Survey and Comparison on Story Construction Techniques Using Deep Learning for Scene Recognition - Uma, Darapu; Kumari, M. Kamala (International Journal of Computer Sciences and Engineering, 2022)
Story construction from deep learning is a naive methodology suitable for the digital forensics, smart video surveillance, and intelligent robotics applications. So far deep learning has been utilized for image recognition and classifications. The sequence of those images and classification of them on a temporal basis leads to the development of knowledge on the entire changes in the scenes and finally end up with a story in which the identified scenes are connected with unambiguous changes. This paper retrieves the pros and cons of the research work on recurrent topic transition GAN for Visual Paragraph Generation and relation pair visual paragraph generation. The Existing algorithms proposed for constructing the stories are RTT GAN, RP GAN and BF GAN. These are implemented with respect to different applications human computer interaction, intelligence robotics, digital forensics etc. The survey of the subjected algorithms gives the transparency of their working principles. The current paper presented the visual representation, description, generation of paragraphs with various methodologies along with the comparison.