Article: Emotion representation and recognition in oil paintings via meta-learning and semantic augmentation Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.60 pp.21 - 40 Abstract: Automated emotion analysis in visual art remains a significant challenge, primarily due to the paucity of annotated data and the profound stylistic and semantic gap between generic image understanding and domain-specific artistic interpretation. This study introduces a novel meta-learning framework enhanced with structured semantic knowledge for few-shot emotion recognition in oil paintings. The proposed model integrates a dual-path architecture: a meta-learning pathway for rapid visual adaptation and a semantic pathway that incorporates contextual art historical knowledge. These pathways are fused through a hierarchical cross-modal attention module, which dynamically aligns visual features with relevant semantic concepts during the learning process. Extensive evaluations on the ArtEmis dataset demonstrate the framework's superior performance, achieving state-of-the-art macro-accuracy of 68.7% (1-shot) and 81.3% (5-shot). The results confirm the model's efficacy in achieving robust, generalisable, and interpretable emotion analysis with limited data, advancing the field of computational art understanding. Inderscience Publishers - linking academia, business and industry through research

Title: Emotion representation and recognition in oil paintings via meta-learning and semantic augmentation

Authors: Wei Li

Addresses: School of Arts and Media, Chuzhou City Vocational College, Chuzhou, 239000, China

Abstract: Automated emotion analysis in visual art remains a significant challenge, primarily due to the paucity of annotated data and the profound stylistic and semantic gap between generic image understanding and domain-specific artistic interpretation. This study introduces a novel meta-learning framework enhanced with structured semantic knowledge for few-shot emotion recognition in oil paintings. The proposed model integrates a dual-path architecture: a meta-learning pathway for rapid visual adaptation and a semantic pathway that incorporates contextual art historical knowledge. These pathways are fused through a hierarchical cross-modal attention module, which dynamically aligns visual features with relevant semantic concepts during the learning process. Extensive evaluations on the ArtEmis dataset demonstrate the framework's superior performance, achieving state-of-the-art macro-accuracy of 68.7% (1-shot) and 81.3% (5-shot). The results confirm the model's efficacy in achieving robust, generalisable, and interpretable emotion analysis with limited data, advancing the field of computational art understanding.

Keywords: oil painting emotion recognition; few-shot learning; meta-learning; semantic enhancement; interpretable artificial intelligence.

DOI: 10.1504/IJICT.2026.153937

International Journal of Information and Communication Technology, 2026 Vol.27 No.60, pp.21 - 40

Received: 07 Feb 2026
Accepted: 13 Mar 2026
Published online: 08 Jun 2026 *

Title: Emotion representation and recognition in oil paintings via meta-learning and semantic augmentation

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