Summary of Deep Ensemble Art Style Recognition, by Orfeas Menis-mastromichalakis et al.
Deep Ensemble Art Style Recognition
by Orfeas Menis-Mastromichalakis, Natasa Sofou, Giorgos Stamou
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of recognizing art style using deep networks. The authors compare the performance of eight different deep architectures (VGG16, VGG19, ResNet50, ResNet152, Inception-V3, DenseNet121, DenseNet201, and Inception-ResNet-V2) on two art datasets, achieving state-of-the-art performance. They also introduce a stacking ensemble method combining the results of first-stage classifiers through a meta-classifier, demonstrating innovation in multiple models extracting and recognizing different characteristics of the input. The authors discuss the impact of data and art styles themselves on model performance, forming a manifold perspective on the problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to recognize what makes one piece of artwork unique compared to others. It’s like trying to figure out what makes a painting look like Monet versus Picasso. The researchers tested different computer programs (called deep networks) to see which ones worked best at doing this job. They found that some programs were much better than others, and they came up with new ways of combining the results from multiple programs to get even better answers. |
Keywords
* Artificial intelligence * Resnet
