A Fusion Model for Artwork Identification Based on Convolutional Neural Networks and Transformers

TL;DR

This paper introduces a fusion model combining CNNs and Transformers to improve artwork identification accuracy by leveraging local and global features, outperforming individual models on Chinese and oil painting datasets.

Contribution

The paper presents a novel fusion approach that integrates CNNs and Transformers for enhanced artwork classification performance.

Findings

Fusion model outperforms individual CNN and Transformer models.

Classification accuracy improved by approximately 9.7% and 7.1%.

F1 scores increased by 0.06 and 0.05.

Abstract

The identification of artwork is crucial in areas like cultural heritage protection, art market analysis, and historical research. With the advancement of deep learning, Convolutional Neural Networks (CNNs) and Transformer models have become key tools for image classification. While CNNs excel in local feature extraction, they struggle with global context, and Transformers are strong in capturing global dependencies but weak in fine-grained local details. To address these challenges, this paper proposes a fusion model combining CNNs and Transformers for artwork identification. The model first extracts local features using CNNs, then captures global context with a Transformer, followed by a feature fusion mechanism to enhance classification accuracy. Experiments on Chinese and oil painting datasets show the fusion model outperforms individual CNN and Transformer models, improving classification accuracy by 9.7% and 7.1%, respectively, and increasing F1 scores by 0.06 and 0.05. The results demonstrate the model's effectiveness and potential for future improvements, such as multimodal integration and architecture optimization.