CROWD-SOURCED FOLK ART CLASSIFICATION MODELS - Hasan, Fazil; Smitha K; Kamalesh, Murari Devakannan; Isarani, Lehar; Biswal, Swetarani; Sobti, Sakshi; Wawage, Pawan (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Folk art is a diverse nexus of place identities, craft systems of knowledge and intergenerational cultural memory. However, its visual heterogeneity, including motifs, materials, techniques, and vocabularies of symbols, creates tremendous problems of scalable digital classification. The conventional manual classification of folk art collections is usually limited by a short pool of expertise, subjective knowledge, and archival diversity. To fill in these gaps, this work presents a folk art classification framework based on a complete crowd-sourced approach to folk art, which incorporates both community engagement and contemporary deep learning architectural designs. Multi source dataset is created by pulling together images of museums, cultural archives, festivals, and local artisan communities. Motifs, geometric patterns, material categories, stylistic markers and region-specific attributes are broken down into structured set of guidelines in terms of annotation. Redundancy checks, worker reliability scoring, means of probabilistic label fusion as well as hierarchical review are used to design a quality-controlled annotation pipeline. The model architecture is based on the integration of the baseline convolutional neural networks with transformer-based visual encoders in order to learn the fine-grained and multi-label folk art descriptors. The probabilistic integration module uses the crowd-sourced annotations in order to address the issue of label noise and enhance robustness. Experimental analysis shows that there is significant improvement in attribute recognition accuracy, hierarchical tagging coherence and cross-regional generalizability.