Research on an Automatic Classification Method for Art Film Scenes Based on Image and Audio Deep Features
Year: 2025
Type: article
Abstract: This paper addresses the challenging task of automatic scene classification in art films, a genre characterized by symbolic visuals, asynchronous audio, and non-linear storytelling. We propose Styloformer, a multimodal transformer architecture designed to integrate visual, auditory, textual, and curatorial signals into a unified representation space. The model combines cross-modal attention, stylistic clustering, influence prediction, and canonicality estimation to handle the semantic and historical complexity of art cinema. Additionally, we introduce a novel module called Historiographic Navigation, which embeds ontological priors and temporal logic to support interpretive reasoning. Evaluated on multiple benchmarks, Styloformer achieves state-of-the-art performance, including 91.85% accuracy and 94.31% AUC on the MovieNet dataset—outperforming baselines such as CLIP and ViT. Ablation studies further demonstrate the importance of each architectural component. Unlike general-purpose video models, our system is tailored to the aesthetic and narrative structure of art films, making it suitable for applications in digital curation and computational film analysis. Styloformer represents a scalable and interpretable approach to understanding artistic media, bridging machine learning with art historical reasoning.
Source: Applied Sciences
DOI: 10.3390/app152312603
Authors: Zhaojun An, Heinz D. Fill
Institutions: Universidad de Buenos Aires, Cornell University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416787093