MACHINE LEARNING FOR ART CRITIQUE GENERATION

The development of artificial intelligence has led to new opportunities to create art critique that is coherent and reacting to context to produce the mimic depth of analysis of humans. The current paper is an in-depth machine learning system that can generate structured, interpretive, and stylistically rich art reviews through the application of state-of-the-art visual comprehension and natural language generation. The suggested system is a combination of the convolutional neural networks (CNNs) and Vision Transformers (ViTs) to extract fine-grained visual evidence, which consists of composition, texture, color harmony, and stylistic cues and fuses them with contextual metadata like the artist background, historical period, and indicative pointers. Multimodal fusion module coordinates these different representations and then sends them to a transformer-based critique generator that is able to generate descriptive, interpretive, comparative, and evaluative text. In order to justify this framework, we assemble a heterogeneous dataset comprising of high-resolution art photographs and professional cura corpora of museums, scholarly publications, and of professional art reviews. The subtle aesthetic judgment and interpretive reference that is lost in technical judgments and lexical richness are made in the form of expert-in-the-loop annotations which are culturally sensitive. The preprocessing methods such as augmentation, normalization, and de-biasing are used to enhance the robustness of the model and minimize the skew in the style. Experiments indicate that, multimodal conditioning greatly increases specificity of critique and conceptual grounding in comparison with vision or text only baselines.