Control With Style: Style Embedding-Based Variational Autoencoder for Controlled Stylized Caption Generation Framework

Automatic image captioning is a computationally intensive and structurally complicated task that describes the contents of an image in the form of a natural language sentence. Methods developed in the recent past focused mainly on the description of factual content in images thereby ignoring the different emotions and styles (romantic, humorous, angry, etc.) associated with the image. To overcome this, few works incorporated style-based caption generation that captures the variability in the generated descriptions. This article presents a style embedding-based variational autoencoder for controlled stylized caption generation framework (RFCG+SE-VAE-CSCG). It generates controlled text-based stylized descriptions of images. It works in two phases, i.e., $ 1)$ refined factual caption generation (RFCG); and $ 2)$ SE-VAE-CSCG. The former defines an encoder-decoder model for the generation of refined factual captions. Whereas, the latter presents a SE-VAE for controlled stylized caption generation. The overall proposed framework generates style-based descriptions of images by leveraging bag of captions (BoCs). More so, with the use of a controlled text generation model, the proposed work efficiently learns disentangled representations and generates realistic stylized descriptions of images. Experiments on MSCOCO, Flickr30K, and FlickrStyle10K provide state-of-the-art results for both refined and style-based caption generation, supported with an ablation study.