NLP MODELS FOR ARTISTIC STATEMENT GENERATION - R.M. Gomathi; Srishti, Pooja; Garg, Prateek; Roselin; Thakker, Hemal; Kaur, Sumeet (ShodhKosh: Journal of Visual and Performing Arts, 2025)
In this paper we propose a multimodal vision-language multimodality detransformer framework for coherent, expressive, and visually grounded artistic statement generation, which takes advantage of multimodal vision- and language modeling on top of a strong transformer-based text generation network. The proposed system is comprised of a visual encoder to interpret compositional and stylistic aspects of an artwork, a fine-tuned transformer decoder that acts as a conceptually rich story engine and a cross-modal fusion module to ensure the alignment between visual clues and linguistic output. Combined with creative and grounding-based reward mechanisms from reinforcement learning, the interpretive depth and style-grounding are further advanced. Using automated similarity measures, multimodality alignment scores and human expert subjectivity measurement, it is shown that the hybrid model greatly improves over traditional captioning and text-only methods at extracting artistry, emotionality and conceptuality. While the method has great potential, challenges exist in dealing with cultural bias, data limitations, interpretive subjectivity and computational demands. Overall, the research brings forward the field of AI-assisted artistic communication and provides a scalable solution to help artists, curators, educators, and digital art platforms to create quality artistic statements.