ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification

• URL: http://arxiv.org/abs/2408.01827v1
• Date: Sat, 3 Aug 2024 17:31:58 GMT
• Title: ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification
• Authors: Mridula Vijendran, Frederick W. B. Li, Jingjing Deng, Hubert P. H. Shum,
• Abstract summary: Painting classification plays a vital role in organizing, finding, and suggesting artwork for digital and classic art galleries. Existing methods struggle with adapting knowledge from the real world to artistic images during training, leading to poor performance when dealing with different datasets. We generate more data using Style Transfer with Adaptive Instance Normalization (AdaIN), bridging the gap between diverse styles. We achieve an impressive 87.24% accuracy using the ResNet-50 backbone over 40 training epochs.
• Score: 9.534646914709018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Painting classification plays a vital role in organizing, finding, and suggesting artwork for digital and classic art galleries. Existing methods struggle with adapting knowledge from the real world to artistic images during training, leading to poor performance when dealing with different datasets. Our innovation lies in addressing these challenges through a two-step process. First, we generate more data using Style Transfer with Adaptive Instance Normalization (AdaIN), bridging the gap between diverse styles. Then, our classifier gains a boost with feature-map adaptive spatial attention modules, improving its understanding of artistic details. Moreover, we tackle the problem of imbalanced class representation by dynamically adjusting augmented samples. Through a dual-stage process involving careful hyperparameter search and model fine-tuning, we achieve an impressive 87.24\% accuracy using the ResNet-50 backbone over 40 training epochs. Our study explores quantitative analyses that compare different pretrained backbones, investigates model optimization through ablation studies, and examines how varying augmentation levels affect model performance. Complementing this, our qualitative experiments offer valuable insights into the model's decision-making process using spatial attention and its ability to differentiate between easy and challenging samples based on confidence ranking.

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